OOK OF ASTRATS - Optimization 2017optimization2017.fc.ul.pt/uploads/1/5/1/4/15140734/...OOK OF...

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BOOK OF ABSTRACTS

Faculdade de Ciências, Universidade de Lisboa

Lisboa, Portugal

September 6-8, 2017

Optimization 2017 – Faculdade de Ciências - Universidade de Lisboa - Portugal 5

Table of contents

Welcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Aims and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Workshop Luís Gouveia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Committees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

General Information and Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Program Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Workshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Conference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Abstracts: Plenary Sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Abstracts: Parallel Sessions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Wednesday . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Thursday . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Friday . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Authors Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Presenting Authors Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

Sessions Chairs Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Sessions Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137


Welcome to Optimization 2017!

On behalf of the Organizing Committee and the Program Committee, it is our great pleasure to welcome

you to the Optimization 2017 at the Faculdade de Ciências da Universidade de Lisboa.

This is the 9th edition of a series of international conferences in optimization organized in Portugal under

the auspices of APDIO (the Portuguese Operations Research Society).

The conference is also sponsored by CMAF-CIO - Centro de Matemática, Aplicações Fundamentais e

Investigação Operacional (Center for Mathematics, Fundamental Applications and Operations Research),

a research unit at Faculdade de Ciências da Universidade de Lisboa.

This year, it is with great pleasure that we host a Workshop celebrating the 60th birthday of our dear

colleague Luís Gouveia (Universidade de Lisboa), a well-known researcher in the field of discrete and

network optimization who significantly contributed to the development of the Optimization/OR research

community in Portugal.

We are very pleased to announce that the conference will have 13 organized sessions, 21 contributed

sessions and 6 plenary sessions with a total of more than 130 oral presentations and participants from

24 different countries.

We organized a social program that includes a Welcome Reception (September 5), a Conference Dinner

(September 7) in a restaurant located in one of the most beautiful city squares in Europe, the Terreiro do

Paço, where you can enjoy a view of the river Tejo. On the the last day of the conference (September 8),

we will have a Boat Tour in the river Tejo, where you can enjoy a different view of the riverside of Lisboa.

We would especially like to thank the six plenary speakers for accepting our invitation and honor us with

their presence.

Also, we would like to thank all the researchers who submitted a presentation for their interest and

participation.

We would also like to thank the conference sponsors.

And last but not least, we would like to thank all the members of the Organizing Committee for their

work, effort and patience with which they contributed to the success of this conference.

We wish all the participants an excellent conference! Enjoy!

All the best,

Miguel Constantino and Pedro Moura


Aims and Scope

This is the 9th edition of a series of international conferences in Optimization organized in Portugal

under the auspices of APDIO (the Portuguese Operations Research Society). The main objective of the

Optimization 2017 conference is to bring together researchers and practitioners from different areas and

backgrounds, but with common interests in optimization. This meeting has international recognition as

an important forum of discussion and exchange of ideas.

Optimization 2017 will host during the first day of the conference, in parallel with the conference

sessions, a Workshop celebrating the 60th birthday of our dear colleague Luís Gouveia (Universidade de

Lisboa), a well-known researcher in the field of discrete and network optimization who significantly

contributed to the development of the Optimization/OR research community in Portugal.

The previous Optimization conferences took place in

• Guimarães (2014)

• Lisboa (2011)

• Porto (2007)

• Lisboa (2004)

• Aveiro (2001)

• Coimbra (1998)

• Braga (1995)

• Coimbra (1991)


Workshop celebrating the 60th birthday of Luís Gouveia

Optimization 2017 will host during the first day of the conference, September 6, in parallel with the

conference sessions, a Workshop celebrating the 60th birthday of our dear colleague Luís Gouveia

(Universidade de Lisboa), a well-known researcher in the field of discrete and network optimization who

significantly contributed to the development of the Optimization/OR research community in Portugal.

Luís Gouveia, born in 1957 in Mozambique, is Full Professor at the

Faculdade de Ciências, Universidade de Lisboa, and former Coordinator of

the Operations Research Center - CIO - at the Faculdade de Ciências from

2003 until 2014. He holds degrees in Applied Mathematics (diploma) from

the Universidade de Lisboa and a PhD in Operations Research.

His current research interests are in network design, ILP model

reformulation and telecommunications.

Luís Gouveia is author of numerous papers in various journals and is also on

the editorial board of some journals, including the position of Associate

Editor of “Networks” and “Computers & OR”.

He is frequently organizing workshops and conferences. Luís Gouveia is also the co-founder of the

Winter School on Network Optimization, in Estoril, Portugal, which is now on its 7th edition. The school's

main objective is to provide an opportunity for PhD students to get together and attend high level

courses in the field.

Homage dinner

At the end of the Workshop, there will be a homage dinner (subject to prior registration) at the

restaurant "Casa do Leão" in Castelo de São Jorge, starting at 19:30. Participants who registered for the

dinner are encouraged to use the metro ticket given at the registration desk, together with the

conference material. Here are some useful directions to get to the restaurant:

• If you are at the conference venue (Faculdade de Ciências), go to the Campo Grande metro station,

choose the Green line towards Cais do Sodré and exit at Rossio station, choosing the exit that

indicates Praça da Figueira.

• From the square Praça da Figueira, follow the street Rua dos Fanqueiros (at the southeast corner of

the square) until number 178 and get on the public elevator that you will find inside the building,

coming out at the street Rua da Madalena.

• From there, you must cross the street and the adjacent two squares, Largo Adelino Amaro da Costa

and Largo do Chão do Loureiro, to the entrance of the supermarket Pingo Doce. Inside you should

take the panoramic elevator (alternatively you can go up the stairs to the right side of the building) to

the top floor.

• Then, turn right onto the street Costa do Castelo, continue along the street Rua do Milagre de Santo

António, and turn left to go up the street Rua Bartolomeu de Gusmão. At the end of this street, turn

left to enter the outer perimeter of the castle walls and from there follow the indications to the

castle.

• At the entrance just show the ticket for the homage dinner that has been delivered to you at the

registration desk.


Committees

Program Committee

• Luís Nunes Vicente, Universidade de Coimbra (Portugal) – Chair

• Agostinho Agra, Universidade de Aveiro (Portugal)

• Cláudio Alves, Universidade do Minho (Portugal)

• Paula Amaral, Universidade Nova de Lisboa (Portugal)

• Miguel F. Anjos, École Polytechnique Montréal (Canada)

• António Pais Antunes, Universidade de Coimbra (Portugal)

• Carlos Henggeler Antunes, Universidade de Coimbra (Portugal)

• Maria Eugénia Captivo, Universidade de Lisboa (Portugal)

• Domingos Cardoso, Universidade de Aveiro (Portugal)

• José Valério de Carvalho, Universidade do Minho (Portugal)

• Jorge Orestes Cerdeira, Universidade Nova de Lisboa (Portugal)

• Miguel Constantino, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• Ana Luísa Custódio, Universidade Nova de Lisboa (Portugal)

• Dalila B. M. M. Fontes, Universidade do Porto (Portugal)

• Fernando A. C. C. Fontes, Universidade do Porto (Portugal)

• Francisco Saldanha da Gama, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• José Fernando Gonçalves, Universidade do Porto (Portugal)

• João Gouveia, Universidade de Coimbra (Portugal)

• Luís Gouveia, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• Joaquim João Júdice, Universidade de Coimbra (Portugal)

• Helena Ramalhinho Lourenço, Universitat Pompeu Fabra (Spain)

• Carlos Luz, Instituto Politécnico de Setúbal (Portugal)

• Joaquim R. R. A. Martins, University of Michigan (USA)

• Pedro Coimbra Martins, Instituto Politécnico de Coimbra (Portugal)

• Maria Cândida Mourão, Instituto Superior de Economia e Gestão, Universidade de Lisboa

(Portugal)

• José Fernando Oliveira, Universidade de Porto, INESC Porto (Portugal)

• Pedro Oliveira, Universidade do Porto (Portugal)

• Marta Pascoal, Universidade de Coimbra (Portugal)

• Margarida Vaz Pato, Instituto Superior de Economia e Gestão, Universidade de Lisboa (Portugal)

• Ana Paula Barbosa Póvoa, Instituto Superior Técnico, Universidade de Lisboa (Portugal)

• Rita Almeida Ribeiro, UNINOVA (Portugal)

• António José Rodrigues, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• Tatiana Tchemisova, Universidade de Aveiro (Portugal)

• Ismael F. Vaz, Universidade do Minho (Portugal)

• Manuel V. C. Vieira, Universidade Nova de Lisboa (Portugal)

14 Optimization 2017 – Faculdade de Ciências - Universidade de Lisboa - Portugal

Organizing Committee

• Miguel Constantino, Faculdade de Ciências, Universidade de Lisboa (Portugal) - Co-Chair

• Pedro Moura, Faculdade de Ciências, Universidade de Lisboa (Portugal) - Co-Chair

• Ana Paias, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• Conceição Fonseca, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• Isabel Martins, Instituto Superior de Agronomia, Universidade de Lisboa (Portugal)

• Luís Nunes Vicente, Universidade de Coimbra (Portugal)

• Pedro Castro, Faculdade de Ciências, Universidade de Lisboa (Portugal)

• Rodrigo Oliveira Marques, CMAF-CIO (Portugal)

Assistants

• Ana Margarida Crespo, Universidade de Lisboa (Portugal)

• Bárbara Tavares, Universidade de Lisboa (Portugal)

• Carolina Gonçalves, Universidade de Lisboa (Portugal)

• Inês Coelho, Universidade de Lisboa (Portugal)

• Inês Novo, Universidade de Lisboa (Portugal)

• Laura Ferreira, Universidade de Lisboa (Portugal)

• Mafalda Ponte, Universidade de Lisboa (Portugal)

• Mariana Cabral, Universidade de Lisboa (Portugal)

• Mário Gomes, Universidade de Lisboa (Portugal)

• Miguel Vieira, Universidade de Lisboa (Portugal)

• Raquel Sousa, Universidade de Lisboa (Portugal)

• Yasmine Oliveira, Universidade de Lisboa (Portugal)


General Information and Guidelines

Language. The conference official language is English.

Conference venue. The conference venue is the Faculdade de Ciências, Lisboa, Portugal. Faculdade de

Ciências is a centenary faculty, founded in April 19, 1911 and is part of the Universidade de Lisboa. Since

1985 it has been located in the campus Cidade Universitária, in the northern part of the city.

The conference will take place in building C3 (plenary sessions and the Workshop) and in building C6

(parallel sessions and the Welcome Reception in the interior courtyard).

All buildings are within a 5 minute walk.

Fig. 1: Campus Map

Registration desk. The registration desk will be located at the atrium of building C6 (see Figure 2). The

registration desk will be open on Tuesday 5th, from 17:30 to 18:30, and on Wednesday from 8:00 to

8:30.

Internet access. Free wireless access is available through the university campus (network name:

eduroam; login: [email protected]; password: Lisboa0608).

Lunches. Every conference participant (regular or PhD student) will receive 3 lunch tickets. During the

conference (6-8 September), lunches will be served in building C7.

Coffee breaks. Coffee, tea, juices, bottled water, pastry and sandwiches will be available during the

conference coffee-breaks in the C3 atrium.

mailto:[email protected]



Fig. 2: C6 ground floor

Fig. 3: C6 first floor

Social events. On September 5 (18:30 - 20:00), there will be a Welcome Reception, served in the interior

courtyard of building C6. It will consist of a cocktail drink and hors d'oeuvre. It will be a great opportunity

to meet again colleagues and friends and catch up.

The Conference Dinner will be served, on September 7, in the restaurant "Museu da Cerveja"

located in the iconic Praça do Comércio/Terreiro do Paço. All participants will have a metro ticket

that will allow them to get to the restaurant by taking the subway to Terreiro do Paço station.

On the last day of the conference and after the Closing Session, we would like to invite all

participants to come and discover a charming Lisboa, full of places of interest that extend through

the Rio Tejo (Tagus River). Come and enjoy a unique panoramic view aboard the Ship Opera in a 3

hour river tour.



Facilities inside the campus. You can find several facilities in the campus:

• an ATM in the open area of building C5;

• a bar/restaurant in the open area of building C5;

• a tapas restaurant “100 Montaditos” on the ground floor of building C7 (facing Campo Grande);

• a bar/snack supermarket in the ground floor of building C7 (facing building C2).

Transportation. The Faculdade de Ciências is located in the campus of the Universidade de Lisboa in the

northern area of Lisboa, only a short distance from two metro stations, “Cidade Universitária” (yellow

line) and “Campo Grande” (green and yellow lines) (see Figure 1). You can buy a metro ticket for € 0,50

and recharge it with trips at € 1,45 each.

Guidelines for session chairs. In the Sessions Index you can find the codes of all the sessions chaired by a

given participant. The list of all session chairs can be found in the Session Chairs Index.

As a session chair please make sure to:

• contact the speakers before the session, to verify who is presenting and to preempt any

technical problems;

• ensure that the session begins and ends on time; all oral presentations last 25 minutes including

2-3 minutes for interaction with the audience;

• ensure that talks respect the program order, to allow participants to jump between sessions; if a

speaker cancels or does not attend, the session schedule should be respected, rather than

shifting every talk backwards.

Guidelines for speakers. In the Presenting Authors Index, next to the presenting author’s name, you can

find the code of the session where the presentation will take place. The session room is given in the

Abstracts Section of the conference book. All session rooms will be equipped with laptops or desktop

computers and overhead projectors. You may use your own laptop to ensure that your presentation use

the right version of the software and fonts installed, so that it looks like what you have planned and

designed. Please follow these guidelines to ensure a successful presentation:

• If you bring your own laptop to your session, bring along the power supply cable. You may need

an adapter to connect your computer to the local voltage (220V) and wall plug type.

• If your laptop is a Mac, bring the required adapter for the external video output.

• Arrive at your session at least 5 minutes before it begins. All presenters in a session should set

up and test the connection to the projector before the session begins. If you need any help just

ask one of the session assistants (students identified by the T-shirts and red badges) in the

room.

• We encourage speakers to have their presentations on a Universal Serial Bus data stick (USB

pen) as a backup.

• Prepare your presentation to fit the allotted time (25 minutes including 2-3 minutes for

interaction with the audience).

• One or more session assistants will be available at each room. You can address the session

assistant for any request or help regarding problems related to audio-visual equipment.

Program Overview

Program Overview


Workshop

Program Overview


Conference

Program

Program - Wednesday


Wednesday, 8:30 – 9:00

Opening Session

Room: 3.2.14

Wednesday, 9:00 – 10:00

Plenary Session I Chair: Francisco Saldanha da Gama

Room: 3.2.14

Stackelberg games and bilevel bilinear optimization problem . . . . . . . . . . . . . . . . . . . . . . . . Martine Labbé

41

Wednesday, 10:40 – 12:20

WA1 Workshop Luís Gouveia Session I Chair: Juan José Salazar Gonzalez

Room: 3.2.14

Capital and loaning constrained project scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Pedro Martins 51

On the robust lotsizing problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cristina Requejo

51

The weighted target set selection problem on cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Raghavan

52

Design of survivable networks with bounded-length-paths . . . . . . . . . . . . . . . . . . . . . . . . . . Ridha Mahjoub

52

Stronger bounds in pseudo-polynomial time for the capacitated vehicle routing problem Juan Jose Salazar Gonzalez

52

WA2 Optimization-Based Control I: Fundamentals Organizer/Chair: Fernando Fontes

Room: 6.2.50

NMPC with economic objectives on target manifolds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Niels van Duijkeren 53

On the design of model predictive control schemes for economic optimization and applications to motion control of robotic vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea Alessandretti

53

On the use of continuous-time models for optimization-based control of constrained nonlinear systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fernando A.C.C. Fontes

54

Program - Wednesday


WA3 Continuous Constrained Optimization

Chair: Ismael Vaz

Room: 6.2.49

A new testbed to benchmark algorithms for continuous constrained optimization . . . . . . . Asma Atamna

54

A stochastic multiple gradient descent algorithm, illustration on a sandwich material optimization problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Quentin Mercier

55

A derivative-based algorithm for constrained minimization . . . . . . . . . . . . . . . . . . . . . . . . . . Cristian Barbarosie

55

Optimization in additive manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ismael Vaz

56

WA4 Multiobjective Optimization

Chair: Marta Pascoal

Room: 6.2.48

An integrated fuzzy c-means clustering and multi criteria decision making methods for evaluating the logistic performance index: a comparative analysis . . . . . . . . . . . . . . . . . . . . Nimet Yapici Pehlivan

56

A fully fuzzy method for multi-objective fractional optimization problems . . . . . . . . . . . . . . Rubi Arya

57

A new algorithm for the multiobjective minimum spanning tree . . . . . . . . . . . . . . . . . . . . . . José Luís Santos

57

Bimaterial 3D printing: formulation and case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marta Pascoal

58

WA5 Optimization in Engineering

Chair: Hideshi Ishida

Room: 6.2.47

Estimation of mature water flooding performance and optimization by using capacitance resistive model and fractional flow model by layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luis Francisco Castillo Gamarra

58

Topology optimization to design magnetic circuits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rtimi Youness

59

An interior point method-based solver for simulation of aircraft parts riveting . . . . . . . . . . Maria Stefanova

60

Non-parametric optimization of time-averaged quantities under small, time-varying forcing: an application to a thermal convection field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hideshi Ishida

60

Program - Wednesday


Wednesday, 13:50 – 15:05

WB1 Workshop Luís Gouveia Session II Chair: Ángel Corberán

Room: 3.2.14

Layered graph approaches for the black-and-white traveling salesman problem . . . . . . . .

Mario Ruthmair 61

Extending and projecting flow models for the (PC)ATSP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pierre Pesneau

62

New decomposition approaches for the two-stage stochastic Steiner tree problem . . . . . . . Ivana Ljubic

62

On the periodic mixed rural postman problem with irregular services . . . . . . . . . . . . . . . . . . Ángel Corberán

63

WB2 Optimization-Based Control II: Algorithms and Applications Organizer/Chair: Fernando Fontes

Room: 6.2.50

Robust a priori planning to the dynamic and stochastic vehicle routing problem . . . . . . . . .

Marcella Bernardo 63

Driving an autonomous car using MPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matthias Knauer

64

An adaptive mesh refinement algorithm with time–dependent criteria for model predictive control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luís Tiago Paiva

64

WB3 Nonlinear Optimization Organizer/Chair: Benoît Pawuels

Room: 6.2.49

A line-search algorithm inspired by the adaptive cubic regularization framework, with a

worst-case complexity O(Ɛ-3/2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . El Houcine Bergou

65

Robust inversion for functional inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohamed Reda El Amri

66

New multi-disciplinary optimization (MDO) approaches based on domain decomposition Benoît Pauwels

66

WB4 Production Scheduling Chair: João Basto

Room: 6.2.48

A scheduling problem and node weighted coloring problem . . . . . . . . . . . . . . . . . . . . . . . . .

Yash Aneja 67

Simultaneously scheduling production, transportation and storage in flexible manufacturing systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seyed Mahdi Homayouni

67

Sequencing of production lines in the footwear industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . João Basto

68

Program - Wednesday


WB5 Equilibrium and Complementarity Chair: Andreas Fischer

Room: 6.2.47

A block active set algorithm for fractional quadratic programming on the unit simplex

and for the symmetric eigenvalue complementarity problem . . . . . . . . . . . . . . . . . . . . . . . . Klaus Schönefeld

69

Newton-type methods for Fritz John systems of generalized Nash equilibrium problems Andreas Fischer

69

Wednesday, 15:15 – 16:15

Plenary Session II Chair: Luís Gouveia

Room: 3.2.14

Quadratic unconstrained binary optimization: some exact and heuristic approaches . . . . .

Giovanni Rinaldi 42

Wednesday, 16:45 – 18:00

WC1 Workshop Luís Gouveia Session III

Chair: Bernard Fortz

Room: 3.2.14

The network design problem with vulnerability constraints . . . . . . . . . . . . . . . . . . . . . . . . . .

Markus Leitner 70

Maximization of protected demand in telecommunication networks using partial disjoint paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amaro de Sousa

70

Combining discretization and Dantzig-Wolfe reformulations: the case of the fixed-charge transportation problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bernard Gendron

71

Connectivity and hop constraints in a social graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bernard Fortz

71

WC2 Variational Inequalities and PDE-Constrained Optimization I

Organizer/Chair: Livia Susu

Room: 6.2.50

On subdifferentials of PDE solution operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Constantin Christof 72

Optimal control of the wave equation with BV-functions presenting . . . . . . . . . . . . . . . . . . Sebastian Engel

72

Optimal control of nonsmooth, semilinear parabolic equations . . . . . . . . . . . . . . . . . . . . . . Livia Susu

73

Program - Wednesday


WC3 Continuous Optimization

Chair: Rohollah Garmanjani

Room: 6.2.49

A gradient sampling method on algebraic varieties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seyedehsomayeh Hosseini

73

The new diagonal Hessian approximation of multi-step gradient-type methods for large scale optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mahboubeh Farid

74

Worst-case complexity analysis of convex nonlinear programming . . . . . . . . . . . . . . . . . . . Rohollah Garmanjani

74

WC4 Railway Optimization

Chair: António Antunes

Room: 6.2.48

Scheduling gantry cranes with transshipment trucks in rail-road container terminals . . . Peng Guo

75

An evolutionary optimization model for solving large-scale line planning problems in railways . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carlos Iglésias

75

Revenue management in a railway company: a case study in Portugal . . . . . . . . . . . . . . . . António Antunes

76

Program - Thursday


Thursday, 9:00 – 10:00

Plenary Session III Chair: Fernando Fontes

Room: 3.2.14

Scenario optimization: how far can we trust data-based decisions? . . . . . . . . . . . . . . . . . . . .

Marco Campi 43

Thursday, 10:40 – 12:20

TA1 Facility Location with Applications Organizer/Chair: Francisco Saldanha-da-Gama

Room: 6.2.50

A stochastic formulation for the simple plant location problem with order . . . . . . . . . . . . .

Xavier Cabezas 78

Outer approximation and submodular cuts for maximum capture facility location problems with random utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ivana Ljubic

78

Supply chain complexity and the network design: location does matter! . . . . . . . . . . . . . . . Mozart B.C. Menezes

79

Service location for unit demand customers: dealing with uncertainty . . . . . . . . . . . . . . . . . Francisco Saldanha-da-Gama

79

TA2 Semidefinite and Semi-infinite Programming

Chair: Tatiana Tchemisova

Room: 6.2.49

SOS versus SDSOS polynomial optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mina Saee Bostanabad

80

Large scale moment/sum-of-squares hierarchy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cédric Josz

81

On optimal properties of special semi-infinite problems arising in parametric optimization Tatiana Tchemisova

81

TA3 Networks I Chair: Maria Teresa Almeida

Room: 6.2.48

k-clubs with diameter constrained spanning trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Filipa Duarte de Carvalho

81

A branch-and-cut algorithm and heuristics for the maximum weight spanning star forest problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luidi Simonetti

82

Stronger extended formulation for the Steiner tree problem . . . . . . . . . . . . . . . . . . . . . . . . . Bartosz Filipecki

83

New models to identify large cohesive groups in networks . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Teresa Almeida

83

Program - Thursday


TA4 Routing I Chair: Maria Cândida Mourão

Room: 6.2.47

Cooperative variable neighborhood search for the vehicle routing problem with pickup

and delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Olcay Polat

84

A variable neighborhood search based solution approach for designing service network of beverage distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leyla Ozgur Polat

84

Performance comparison of modeling approaches for the steering of international roaming problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria da Conceição Fonseca

85

Arc routing involving dissimilarity issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maria Cândida Mourão

86

TA5 Non-Linear MIP Chair: Pedro Castro

Room: 6.2.46

Mixed integer quadratic programming and an application in workload assignment . . . . . .

Melis Mumcuoglu 86

A time transformation approach in hybrid vehicles optimal design . . . . . . . . . . . . . . . . . . . . Massimo De Mauri

87

Reliable convex relaxation techniques for global optimization . . . . . . . . . . . . . . . . . . . . . . . . Frederic Messine

88

Global optimization algorithm for MIQCPs featuring dynamic piecewise relaxations . . . . . Pedro Castro

88

TA6 Sectorization and Parking Chair: Joana Cavadas

Room: 6.2.45

Benders decomposition for the multi-period sales districting problem . . . . . . . . . . . . . . . . .

Saranthorn Phusingha 89

Sectorization problems with multiple criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luís Miguel Bandeira

90

Effect of the learning factors on the dynamic assignment problem of parking slots . . . . . . Mustapha Ratli

91

Game-theoretic approach to transit and parking planning under competition . . . . . . . . . . . Joana Cavadas

91

Thursday, 13:50 – 15:05

TB1 Copositive Optimization I Organizer/Chair: Paula Amaral

Room: 6.2.50

Copositive approach to adjustable robust optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Markus Gabl 92

Quadratic optimization with uncertainty in the objective function . . . . . . . . . . . . . . . . . . . . Michael Kahr

92

An exact copositive representation for the discrete ordered median problem . . . . . . . . . . . Justo Puerto

93

Program - Thursday


TB2 Graphs and Optimization Organizer/Chair: Domingos M. Cardoso

Room: 6.2.49

The train frequency compatibility problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Jorge Orestes Cerdeira 94

A semidefinite programming approach to the 2-club problem . . . . . . . . . . . . . . . . . . . . . . . . Carlos J. Luz

95

Lexicographic polynomials of graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Domingos M. Cardoso

95

TB3 Variational Inequalities and PDE-Constrained Optimization II Organizer/Chair: Livia Susu

Room: 6.2.48

Ill-posed backward nonlinear hyperbolic evolution Maxwell’s equations . . . . . . . . . . . . . . .

Dehan Chen 96

Total variation regularization of multi-material topology optimization . . . . . . . . . . . . . . . . Florian Kruse

96

Inverse point source location with the Helmholtz equation . . . . . . . . . . . . . . . . . . . . . . . . . . . Philip Trautmann

97

TB4 Derivative Free Optimization Organizer/Chair: Margherita Porcelli

Room: 6.2.47

Rethinking the benchmarking of derivative free optimizers . . . . . . . . . . . . . . . . . . . . . . . . . .

Anne Auger 97

MultiGLODS: global and local multiobjective optimization using direct search . . . . . . . . . . Ana Luísa Custódio

98

Optimizing structured problems without derivatives and other new developments in the BFO package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Margherita Porcelli

98

TB5 Clustering Chair: Graça Gonçalves

Room: 6.2.46

q-vars: a new heuristic to select the relevant features for clustering . . . . . . . . . . . . . . . . . . .

Stefano Benati 99

New results in clustering data that are connected through a network . . . . . . . . . . . . . . . . . . Antonio Manuel Rodríguez-Chía

99

Comparative study of mathematical formulations for the K clusters with fixed cardinality problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graça Gonçalves

100

TB6 Facility Location

Chair: Isabel Correia

Room: 6.2.45

A continuous formulation for the multi-row facility layout problem with rectilinear

distances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manuel Vieira

100

Ranking-based random search algorithm for discrete competitive facility location . . . . . . . Algirdas Lancinskas

101

A dynamic capacitated location problem with modular capacity adjustments and flexible demand satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Isabel Correia

101

Program - Thursday


Thursday, 15:15 – 16:15

Plenary Session IV Chair: Paula Amaral

Room: 3.2.14

On gaps and dots - duality and attainability in conic optimization . . . . . . . . . . . . . . . . . . . .

Immanuel Bomze 44

Thursday, 16:45 – 18:00

TC1 Copositive Optimization II Organizer/Chair: Paula Amaral

Room: 6.2.50

Factorizations for completely positive matrices based on alternating projections . . . . . . . .

Patrick Groetzner 102

On regular simplicial division in branch-and-bound algorithms for copositivity detection Leocadio G. Casado

103

Completely positive formulations for minimax fractional quadratic problems . . . . . . . . . . . Paula Amaral

103

TC2 Stochastic and Randomized Algorithms Organizer/Chair: Clément Royer

Room: 6.2.49

Stochastic variance reduced methods based on sketching and projecting . . . . . . . . . . . . . . .

Robert M. Gower 104

Upper-confidence Frank-Wolfe algorithms for convex bandit optimization: fast rates . . . . Vianney Perchet

104

Including inexact second-order aspects in first-order methods for nonconvex optimization Clément Royer

105

TC3 Optimization Theory Chair: Claudio Gentile

Room: 6.2.48

Bases of the subaditive cone and benders decomposition for the dual of the b-complementary multisemgroup problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eleazar Madriz

105

Bounds for ranks of polygons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . António Goucha

106

Matrix decomposition and the perspective reformulation of nonseparable quadratic programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Claudio Gentile

107

Program - Thursday


TC4 Health Care Optimization Chair: Maria Eugénia Captivo

Room: 6.2.47

Optimizing ambulance dispatching and relocation using a preparedness function . . . . . . .

Ana Sofia Carvalho 107

Comparison of different polices for multi-agent kidney exchange programs . . . . . . . . . . . . Xenia Klimentova

108

Different perspectives for a surgical case assignment problem . . . . . . . . . . . . . . . . . . . . . . . . Maria Eugénia Captivo

109

TC5 Urban Transportation Chair: Marta Mesquita

Room: 6.2.46

A math-heuristic for bus driver rostering: generation, evolution and repair . . . . . . . . . . . . .

Vítor Barbosa 110

Multiple-period interval synchronization in urban public transport . . . . . . . . . . . . . . . . . . . Katarzyna Gdowska

111

A decompose-and-fix heuristic for re-rostering bus drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . Marta Mesquita

111

TC6 Travelling Salesman Problem

Chair: Daniel Santos

Room: 6.2.45

Models for the family traveling salesman problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Raquel Bernardino 112

New inequalities and formulations for the double TSP with multiple stacks . . . . . . . . . . . . . Michele Barbato

113

A new formulation for the Hamiltonian p-median problem . . . . . . . . . . . . . . . . . . . . . . . . . . . Daniel Santos

113

Program - Friday


Friday, 9:00 – 10:00

Plenary Session V Chair: Domingos Cardoso

Room: 3.2.14

Continuation in optimization: from interior point methods to big data . . . . . . . . . . . . . . . .

Jacek Gondzio 46

Friday, 10:40 – 12:20

FA1 Recent Advances in First-Order Methods and Applications

Organizer/Chair: Clément Royer

Room: 6.2.50

Iterative regularization for general inverse problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Guillaume Garrigos 115

Activity identification and local linear convergence of forward-backward-type methods Jingwei Liang

115

Scale-free texture segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nelly Pustelnik

116

Accelerated alternating descent methods for Dykstra-like problems . . . . . . . . . . . . . . . . . . . Samuel Vaiter

116

FA2 Mixed Integer Problems

Organizer/Chair: Agostinho Agra

Room: 6.2.49

Economic lot-sizing problem with remanufacturing option: complexity and algorithms . . .

Ashwin Arulselvan 117

Vehicle routing problem in wireless sensor networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luis Flores

117

A decomposition algorithm for robust lot sizing problem with remanufacturing option . . . Öykü Naz Attila

118

Policies for the robust lot-sizing problem with perishable products . . . . . . . . . . . . . . . . . . . . Agostinho Agra

118

FA3 Routing II Chair: Germán Paredes-Belmar

Room: 6.2.48

Hybrid heuristic approaches for a stochastic production-inventory-routing problem . . . . . .

Filipe Rodrigues 119

An iterative optimization approach for drone supported travelling salesman problem . . . . Emine Es Yurek

120

Utilization of internet of things for routing in city logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . Katarzyna Gdowska

120

The HAZMAT distribution problem with multiple products . . . . . . . . . . . . . . . . . . . . . . . . . . . Germán Paredes-Belmar

121

Program - Friday


FA4 Networks II

Chair: Dalila B. M. M. Fontes

Room: 6.2.47

Robustness assessment of complex networks based on the Kirchhoff index . . . . . . . . . . . . .

Alessandra Cornaro 121

Locating a cluster head for minimum-power under symmetric range assignment . . . . . . . . Kevin Prendergast

122

Heuristics solutions for the maximum edge weight clique problem: a quadratic approach Dalila B. M. M. Fontes

123

FA5 Optimization Applications

Chair: Abílio Lucena

Room: 6.2.46

Directed clustering in weighted networks: a new perspective . . . . . . . . . . . . . . . . . . . . . . . . .

Gian Paolo Clemente 123

Genetic algorithm for intrusion detection of pervasive and ubiquitous environments . . . . . Lynda Sellami

124

On the dynamics of computer viruses transmission using an epidemiological approach . . . M. Teresa T. Monteiro

125

Analytical models to estimate connectivity and value in the international trade of supplies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Abílio Lucena

125

Friday, 13:50 – 14:50

Plenary Session VI Chair: Luís Nunes Vicente

Room: 3.2.14

Quasi-Newton methods: block updates, adaptive step sizes, and stochastic variants . . . . .

Donald Goldfarb 47

Friday, 14:50 – 15:15

Closing Session Room: 3.2.14

Abstracts

Plenary Sessions

Plenary Sessions


Wednesday, 9:00 – 10:00

Plenary I

Chair: Francisco Saldanha da Gama Room: 3.2.14

Stackelberg games and bilevel bilinear optimization problem

Martine Labbé, Université Libre de Bruxelles, [email protected]

Martine Labbé is a full professor at the Université Libre de Bruxelles (ULB),

see http://homepages.ulb.ac.be/~mlabbe/. She is Professor of Operations

Research at the Computer Science Department of the Faculty of Sciences.

From 2007 to 2011, she was Dean of the Faculty of Sciences. Her main

research area is combinatorial optimization, including graph theory and

integer programming problems and with a particular emphasis on location

and network design problems. She is also specialized in bilevel programming

and studies pricing problems and Stackelberg games. She served on the

editorial boards of Discrete Optimization, Journal on Combinatorial

Optimization, Operations Research, Operations Research Letters and

Transportation Science. She is now the Editor in Chief of the EURO Journal on Computational

Optimization. She is the author or coauthor of more than 100 papers published in international

journals. In 2007-2008, she was president of EURO, the Association of European Operational Research

Societies. She was, in 2014 and 2015, Vice-Chair of the SIAM Activity Group on Optimization

(SIAG/OPT).

Abstract

Stackelberg games confront contenders with opposed objectives, each wanting to optimize their

rewards. Decision-making parties involve a party with the capacity of committing to a given action or

strategy, referred to as the leader, and a party responding to the leader's action, called the follower.

The objective of the game is for the leader to commit to a reward-maximizing strategy anticipating that

the follower will best respond.

Finding the optimal mixed strategy of the leader in a Stackelberg game is NP-hard when the leader

faces one out of several followers, and polynomial when there exists a single follower. Additionally,

games in which the strategies of the leader consist in covering a subset of at most K targets and the

strategies of the followers consist in attacking some target are called Stackelberg security games and

involve an exponential number of pure strategies for the leader.

A Stackelberg game can be modeled as a bilevel bilinear optimization problem which can be

reformulated as a single level mixed integer nonlinear program (MINLP). We present different

reformulations of this MINLP and compare their LP relaxations from both theoretical and

computational points of view.


http://homepages.ulb.ac.be/~mlabbe/

Plenary Sessions


Wednesday, 15:15 – 16:15

Plenary II

Chair: Luís Gouveia Room: 3.2.14

Quadratic unconstrained binary optimization: some exact and heuristic

approaches

Giovanni Rinaldi, IASI Roma, CNR, [email protected]

Giovanni Rinaldi is a Research Director of the Italian National Research

Council (CNR). He received a master degree in System Science at the

Engineering School of the University of Rome in 1976. In 1982 he got a

tenured position as a researcher at the Institute on System Analysis and

Compute Science (IASI) of the CNR, of which is the director from 2014. He

directed the same Institute from 1998 to 2009. He was Visiting Professor at

the New York University and at the universities of Augsburg, Cologne and

Heidelberg. His main research interests are in combinatorial optimization, in

particular in the study of structural properties of hard problems and in their exploitation to design

efficient exact algorithms. His favorite problems are the traveling salesman, the vehicle routing and

The max-cut problem.

Abstract

Quadratic unconstrained binary optimization (QUBO), i.e., the problem of minimizing a quadratic form

in binary variables, is one on the most studied and best known hard discrete optimization problems.

Due to the its great interest among the optimizers, several approaches, also of a quite diverse nature,

have been proposed to find good or provably good solutions, which makes it also very interesting as a

benchmark problem for new algorithmic ideas. Very recently, QUBO has received a renewed attention

since a dedicated hardware, based on quantum annealing, has been realized that yields good solution

in amazingly short times for some particular instances (the Chimera graphs). In the talk some of the

most successful among these approaches are reviewed.

Plenary Sessions


Thursday, 9:00 – 10:00

Plenary III

Chair: Fernando Fontes Room: 3.2.14

Scenario optimization: how far can we trust data-based decisions?

Marco Campi, Università degli Studi di Brescia, [email protected]

Marco Claudio Campi is professor of inductive methods at the University

of Brescia, Italy. He is the chair of the Technical Committee IFAC on

Modeling, Identification and Signal Processing (MISP) and has been in

various capacities on the Editorial Board of Automatica, Systems and

Control Letters and the European Journal of Control. Marco Campi is a

recipient of the "Giorgio Quazza" prize, and, in 2008, he received the IEEE

CSS George S. Axelby outstanding paper award for the article "The

Scenario Approach to Robust Control Design". He has delivered plenary

and semi-plenary addresses at major conferences including SYSID, MTNS,

and CDC. Currently he is a distinguished lecturer of the Control Systems

Society. Marco Campi is a Fellow of IEEE, a member of IFAC, and a

member of SIDRA.

Abstract

Knowledge is grounded in experience, and the scenario approach studies how experience can be used

to optimize our decisions in relation to prescribed goals. A fundamental element in decision-making is

the presence of uncertainty so that in a real world the same decision never generates exactly the same

outcome. In this talk we discuss the link between complexity of the decision and its robustness against

uncertainty and show that tight evaluations on the robustness can be made with virtually no

knowledge on the underlying mechanisms by which uncertainty is generated.

Plenary Sessions


Thursday, 15:15 – 16:15

Plenary IV

Chair: Paula Amaral Room: 3.2.14

On gaps and dots - duality and attainability in conic optimization

Immanuel Bomze, Universität Wien, [email protected]

Immanuel M. Bomze was born in Vienna, Austria, in 1958. He received the

degree Magister rerum naturalium in Mathematics at the University of

Vienna in 1981. After a postgraduate scholarship at the Institute for

Advanced Studies, Vienna from 1981 to 1982, he received the degree

Doctor rerum naturalium (PhD) in Mathematics at the University of Vienna.

After his Habilitation 1987, he held several visiting research positions at

various research institutions across Europe, the Americas, Asia and

Australia. He also gained some practical operations research experience

during his work as a research mathematician in the Business & Marketing

Research/Operations Research group of the national incumbent

telecommunication operator Telekom Austria 2002-2004. Since 2004, he holds a chair (full professor) of

Applied Mathematics and Statistics at the University of Vienna and since 2009, Bomze serves as the

Study Director of the Abraham-Wald-PhD program in Statistics and Operations Research, located at

the Faculty of Business, Economics, and Statistics at this university. Bomze's research interests are in

the areas of nonlinear optimization, qualitative theory of dynamical systems, game theory,

mathematical modelling and statistics, where he has edited one and published four books, as well as

over 100 peer-reviewed articles in scientific journals and monographs. The list of his co-authors

comprises over seventy scientists from more than a dozen countries in four continents. In 2014 he was

elected Fellow of EurOpt, the Continuous Optimization Working Group of EURO, the Association of

European Operational Research Societies. As a member of program and/or organizing committees, he

co-organized various scientific events and he is an Associate Editor for five international journals. For

several science foundations and councils (based in Canada, the Czech Republic, Germany, Great Britain,

Hong Kong, Israel, Italy, the Netherlands, Norway, Portugal, Singapore, Spain, USA), and for almost 50

scientific journals he acted as a reporting referee. Until the end of 2017 he serves as an Editor of the

European Journal of Operational Research, one of the worldwide leading journals in the field. Bomze

co-founded the Vienna Center of Operations Research (VCOR) and serves as its co-director. Recently he

was nominated as a candidate for the president-elect of EURO, who will commence office in 2018.

Abstract

One of the most powerful methods for obtaining efficient bounds for hard optimization problems is

based upon (Lagrangian) duality, i.e. linearly combining the original objective and (some of) the

constraints. In general there will be a gap between the best such bound, i.e., the optimal dual value,

and the originally sought optimal primal value. Most frequently, a positive duality gap is blamed upon


Plenary Sessions


failure of convexity; however, even in a linear context (over convex cones), positive or even infinite

duality gaps can also occur (in sharp contrast to the familiar linear optimization over polyhedra), and

this is due to a failure of closedness of related sets, rather than a convexity problem. Likewise,

attainability can fail in this context as witnessed by the celebrated Frank-Wolfe theorem and related all-

quadratic counterexamples over convex feasible sets.

Moreover, the dual problem significantly depends on the choice how to model the primal problem: one

and the same primal problem can have several dual formulations with different gaps and attainability

properties; the talk will address a recently investigated hierarchy of these dual models: we consider a

primal-dual pair of conic optimization which deals with optimizing a linear function over an affine

subset of a closed, convex cone. Well investigated and widely used examples include the positive

orthant (LP), the semidefinite cone (SDP), the copositive cone (COP) or the completely positive cone

(CPP).

The latter two occur in reformulations or tight relaxations of hard optimization problems, among them

indefinite quadratic (fractional or binary) programs, and several combinatorial optimization problems.

This talk presents a construction which transforms any such primal-dual pair with an arbitrary (zero,

positive or infinite) duality gap into another pair with the same optimal objective values, where either

the primal or the dual optimal value is not attained. The construction basically doubles the size of the

problems and establishes all possible combinations of gaps and attainability.

Further, a quite recent fresh look at mixed-binary quadratic problems will be offered, establishing a

hierarchy of dual problems with different tightness of dual bounds and time permitting, we will shortly

address the Semi-Lagrangian tightening of all-quadratic problems with a copositive reformulation. As is

well-known, (COP) or (CPP) cannot be solved directly since the involved cones are intractable, but they

can be approximated to arbitrary accuracy, e.g. the copositive cone from inside by polyhedral cones

yielding a sequence of LPs which all tighten classical Lagrangian bounds considerably in case of a

positive classical duality gap.

Based upon joint work with: Jianqiang Cheng, Univ. Arizona; Peter J.C. Dickinson, Univ. Twente; Abdel Lisser, Univ. Paris Sud; Werner Schachinger and Gabriele Uchida, Univ. Vienna.

Plenary Sessions


Friday, 9:00 – 10:00

Plenary V

Chair: Domingos Cardoso Room: 3.2.14

Continuation in optimization: from interior point methods to big data

Jacek Gondzio, University of Edinburgh, J. [email protected]

Jacek Gondzio is Professor of Optimization at the School of Mathematics at

the University of Edinburgh. Prof Gondzio is interested in the theory and

implementation of optimization methods for linear, quadratic and

nonlinear programming.

He is best known for his contributions to interior point methods for very

large scale optimization. He also works on a development of new

algorithms for combinatorial optimization and on the use of linear algebra

techniques and sparse matrix factorisation methods applied in

optimization.

His interests include the use of parallel and distributed computing for solving real-life very large

optimization problems arising in different applications.

Abstract

In this talk we will discuss similarities between two homotopy-based approaches:

- (inexact) primal-dual interior point method for LP/QP, and

- preconditioned Newton conjugate gradient method for big data optimization.

Both approaches rely on clever exploitation of the curvature of optimized functions and deliver

efficient techniques for solving optimization problems of unprecedented sizes. We will address both

theoretical and practical aspects of these methods.

References:

[1] J. Gondzio, Interior point methods 25 years later, European Journal of Operational Research 218

(2012) pp. 587--601. DOI: 10.1016/j.ejor.2011.09.017

[2] J. Gondzio, Convergence analysis of an inexact feasible interior point method for convex quadratic

programming, SIAM Journal on Optimization 23 (2013) No 3, pp. 1510--1527. DOI: 10.1137/120886017

[3] K. Fountoulakis and J. Gondzio, A second-order method for strongly convex L1-regularization

problems, Mathematical Programming 156 (2016), pp. 189--219. DOI: 10.1007/s10107-015-0875-4

[4] K. Fountoulakis and J. Gondzio, Performance of first- and second-order methods for L1-regularized

least squares problems, Computational Optimization and Applications 65 (2016), pp. 605--635. DOI:

10.1007/s10589-016-9853-x.

Plenary Sessions


Friday, 13:50 – 14:50

Plenary VI

Chair: Luís Nunes Vicente Room: 3.2.14

Quasi-Newton methods: block updates, adaptive step sizes, and stochastic

variants

Donald Goldfarb, Columbia University, [email protected]

Professor Donald Goldfarb, the Avanessians Professor of IEOR at

Columbia, is internationally recognized for his contributions to the field

of optimization, and in particular for the development and analysis of

efficient and practical algorithms for solving various classes of

optimization problems. His most well-known and widely used algorithms

include steepest-edge simplex algorithms for linear programming, the

BFGS quasi-Newton method for unconstrained optimization, and the

Goldfarb-Idnani algorithm for convex quadratic programming. He has

also developed simplex and combinatorial algorithms for network flow

problems, and interior-point methods for linear, quadratic and second-

order cone programming. His recent work on robust optimization for

portfolio selection, algorithms for image de-noising, compressed sensing and machine learning is very

highly cited. He is a SIAM Fellow (2012), was awarded the Khachiyan Prize (2013) and the Prize for

Research Excellence in the Interface between OR and CS (1995) by INFORMS, and is listed in The

World’s Most Influential Scientific Minds, 2014, as being among the 99 most cited mathematicians

between 2002 and 2012.

Abstract

We discuss several recent variants that we have developed for quasi-Newton methods and in particular

for the BFGS method. The primary motivation for these developments is the need to solve

optimization problems that arise in machine learning, which because of the enormous amounts of data

involved in each computation of the function and gradient, usually require a stochastic optimization

approach. The issues that we address in this talk are: (i) the use of sketching (i.e., Hessian actions) and

block-updates to incorporate (noisy) curvature information; (ii) the determination of adaptive step sizes

to avoid line searches for strictly convex self-concordant functions; and (iii) the development of

stochastic BFGS variants for both convex and non-convex stochastic optimization problems. In this talk,

several new theoretical results will be presented and illustrated by computational results.


Abstracts

Parallel Sessions

Wednesday Sessions


Wednesday

10:40 – 12:20

WA1 Workshop Luís Gouveia Session I Workshop

Chair: Juan Jose Salazar Gonzalez Room: 3.2.14

1 - Capital and loaning constrained project scheduling

Pedro Martins, Polytechnic Institute of Coimbra and CMAF-CIO, [email protected]

Abstract

Project scheduling together with cash-flows has long been discussed in the literature, but less attention

has been given to borrowing strategies for supporting projects’ costs. In many project scheduling

practical problems, loaning is not a choice but the unique option for initiating the process. In effect, an

adequate loaning strategy is crucial, not just for launching the project but also for guaranteeing its

financial success.

In this presentation we discuss project scheduling along a fixed horizon cash-flow stream that

incorporates loaning strategies. There is an initial capital made available by the project owner, to be

used to support the activities’ costs, together with cash in-flows brought by loans. These loans are

assumed to be fully amortized within the given time horizon. After completion, the activities start

generating profits, feeding back the financial stream. In addition, the project is not forced to be fully

implemented, in the sense that the activities are allowed not to perform, although assuming the

original precedence relationships. So, the problem is to determine when to launch the elected activities

such that the cash-flow at the end of the planning horizon is maximized.

We present a mixed integer linear programming model for the problem and discuss applications

involving different environments and specificities.

2 - On the robust lotsizing problem

Cristina Requejo, University of Aveiro, [email protected]

Co-author(s): Agostinho Agra, University of Aveiro, [email protected]; Filipe Rodrigues, University of Aveiro,

[email protected]

Abstract

We consider the well known robust lotsizing problem where demands are uncertain. The demand can

be satisfied by production, by inventory held in stock or backlogged. A recourse model is considered

where the production decisions are first stage decisions and the stock and backlog variables are

adjustable to the demands. For the uncertainty set, we consider the classical budget polytope. In this

talk we compare two classical approaches for this problem: the minmax approach from Bienstock and

Ozbay (2008) and the dualization from Bertsimas and Thiele (2006).

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3 - The weighted target set selection problem on cycles

S. Raghavan, Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, USA, [email protected]

Co-author(s): Rui Zhang, Leeds School of Business, University of Colorado Boulder, USA, [email protected]

Abstract

The study of viral marketing strategies on social networks has become an area of significant research

interest. In this setting we consider a combinatorial optimization problem referred to as the weighted

target set selection (WTSS) problem. In the WTSS problem, we are given a connected undirected graph

),( EVG , where for each node i in V , there is a threshold ig between 1 and )deg(i (the degree of

node i ) and a positive weight, denoted by ib . All nodes are inactive initially. We select a subset of

nodes, the target set, and they become active. The cost of selection of a node i is given by its weight

ib . After that, in each step, we update the state of nodes by the following rule: an inactive node i

becomes active if at least ig of its neighbors are active in the previous step. The goal is to find the

minimum weight target set while ensuring that all nodes are active by the end of this activation

process. The WTSS problem is known to be NP-hard. Motivated by the desire to develop a better

understanding of fundamental problems in social network analytics, we focus on a special case where

the underlying graph is a cycle. We propose a linear time algorithm for the WTSS problem on cycles.

More importantly, we provide a complete characterization of the polytope of the WTSS problem on

cycles (i.e., the convex hull of 0/1-incidence vectors of all feasible solution of the WTSS problem on

cycles). These results provide a building block for developing exact methods for tackling more general

instances of this important problem in social network analytics.

4 - Design of survivable networks with bounded-length-paths

Ridha Mahjoub, LAMSADE, Université Paris-Dauphine, [email protected]

Abstract

We discuss, from a polyhedral point of view, variants of the k -connected subgraph problem with

bounded-length-paths. We give integer programming formulations and introduce several classes of

valid inequalities along with necessary conditions and sufficient condition for these inequalities to be

facet defining. We also discuss separation routines for these classes of inequalities. Using this we

propose Branch-and-Cut algorithms and present some experiment results.

5 - Stronger bounds in pseudo-polynomial time for the capacitated vehicle

routing problem

Juan Jose Salazar Gonzalez, Universidad de La Laguna, [email protected]

Co-author(s): Adam Letchford, Lancaster University, [email protected]

Abstract

The Capacitated Vehicle Routing Problem (CVRP) is a classic combinatorial optimisation problem, for

which many heuristics, relaxations and exact algorithms have been proposed. Luís Gouveia contributed

Wednesday Sessions


to determine interesting formulations for the CVRP. Since the it is NP-hard in the strong sense, a natural

research topic is relaxations that can be solved in pseudo-polynomial time. We consider several old and

new relaxations of this kind, all of which are based on column generation. Computational experiments

demonstrate that the best of our relaxations yield extremely tight lower bounds.

10:40 – 12:20

WA2 Optimization-Based Control I: Fundamentals

Organized Session

Organizer/Chair: Fernando Fontes Room: 6.2.50

1 - NMPC with economic objectives on target manifolds

Niels van Duijkeren, KU Leuven, Department of Mechanical Engineering, [email protected]

Co-author(s): Timm Faulwasser, Karlsruhe Institute of Technology, Institute for Applied Informatics,

[email protected]; Goele Pipeleers, KU Leuven, Department of Mechanical Engineering

Jan Swevers, KU Leuven, Department of Mechanical Engineering

Abstract

This talk presents a predictive approach for stabilizing a target manifold in the state-space of nonlinear

dynamical systems while optimizing for economic performance on this manifold. The control design is

based on transverse normal form descriptions of the dynamics. A stabilizing transversal NMPC acts as

an outer control-loop to stabilize a neighborhood of the manifold. A tangential inner loop NMPC refines

the remaining degrees of freedom in the benefit of economic performance without compromising

manifold stability. The two-stage approach is especially interesting for its application on embedded

systems when the computationally attractive stabilizing NMPC formulation is augmented with an

"approximate" economic refinement step. We discuss the stability and performance properties of the

resulting control scheme, and show its efficacy in an illustrative example.

2 - On the design of model predictive control schemes for economic

optimization and applications to motion control of robotic vehicles

Andrea Alessandretti, [email protected]

Abstract

In a classic tracking-MPC framework, where the main goal is to steer the state of the system to a

desired state trajectory, the performance index is properly chosen to penalize the distance from the

current state to a desired one. In order to capture more complex control objectives, in recent years a

growing attention has been dedicated to a new class of controllers that goes under the name of

Economic-MPC. Here, the term economic is used to highlight that the performance index is a general

index of interest that we wish to minimize, e.g., economic, that does not denote the distance to a

desired set point. This setting makes full use of the unique potentialities of optimization-based control

strategies and gives space to many applications. This talk addresses a set of tools for the design of

provably correct MPC controllers for the case where the performance index is of the tacking-MPC type,


Wednesday Sessions


purely economic, or a combination of the two. The proposed strategies are applied to a range of

motion control problems for underactuated vehicles, such as trajectory-tracking/path-following,

energy-efficient trajectory tracking, target estimation and tracking via highly observable trajectories,

and others.

3 - On the use of continuous-time models for optimization-based control

of constrained nonlinear systems

Fernando A.C.C. Fontes, Universidade do Porto, [email protected]

Abstract

In the context of optimal control and sampled-data model predictive control, we discuss a few

phenomena that are better understood when using continuous-time models (stability, discontinuous

feedbacks, bang-bang control, path-following, impulsive systems).

We also discuss ways to guarantee that pathwise state-constraints (enforced at all times) are in fact

satisfied when a finite number of verifications is used.

10:40 – 12:20

WA3 Continuous Constrained Optimization Contributed Session

Chair: Ismael Vaz Room: 6.2.49

1 - A new testbed to benchmark algorithms for continuous constrained

optimization

Asma Atamna, Inria, [email protected]

Co-author(s): Phillipe Sampaio, Inria, [email protected]; Nikolaus Hansen, Inria,

[email protected]; Dimo Brockhoff, Inria, [email protected]; Anne Auger, Inria, [email protected]

Abstract

We present a new testbed of constrained problems for benchmarking continuous optimization

algorithms. This testbed is provided by the COCO (COmparing Continuous Optimisers) platform and

consists in 48 constrained problems built from 8 COCO objective functions, by adding a varying number

of linear inequality constraints then applying nonlinear transformations to the resulting constrained

problems. In our context, the constraints are handled as black-boxes, in particular, the mathematical

definition of the constraint functions is unavailable to the solver.

This work is motivated by the need to provide a well-documented testbed for constrained optimization

where (i) the user has a full understanding of the difficulties of the optimization problem at hand, (ii) a

constrained problem can be generated for any given dimension n of the search space and any number

of constraints (scalability), (iii) the user can easily process and visualize their data.

We illustrate how the constrained problems of our testbed are constructed and, as a test case, we

benchmark the well-known MATLAB solver for constrained optimization, fmincon, on the proposed

testbed.




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2 - A stochastic multiple gradient descent algorithm, illustration on a

sandwich material optimization problem

Quentin Mercier, Onera, [email protected]

Co-author(s): Fabrice Poirion, Onera, [email protected]; Jean-Antoine Désidéri, Inria,

[email protected]

Abstract

In this talk, we consider a new method for solving multiobjective optimisation problems where the

objectives are written as expectations of random functions. To ensure a Pareto equilibrium of a design

for such a problem without estimating the expectations, we propose an extension of the classical

stochastic gradient algorithm to the multiobjective case. The extension is based on the existence of a

common descent vector built using the objective gradients and defined in the Multiple Gradient

Descent Algorithm (MGDA) for deterministic problems. Considering classic hypothesis of the stochastic

gradient algorithm, the mean square and almost sure convergence of this new algorithm can be

proven. The use of subdifferiential approaches makes this new algorithm able to handle non regular

objective without the loss of convergence properties.

A three layer sandwich material optimization is proposed as an illustration of the algorithm under

different optimization contexts. The sandwich must be optimized under four optimization variables

knowing that some of the constitutive material properties are considered as random variables.

Particularly, an example of an optimisation under constraints will be presented.

3 - A derivative-based algorithm for constrained minimization

Cristian Barbarosie, CMAF-CIO, FCUL, Universidade de Lisboa, [email protected]

Co-author(s): Sérgio Lopes, ISEL, Instituto Politécnico de Lisboa, [email protected]

Abstract

We propose an algorithm for minimizing an objective functional subject to constraints. The method is

based on the derivatives of the involved functions. It accepts a step aiming at minimizing the objective

functional (this may come from the steepest descent method, or from some conjugated gradient

methods, or others), to which it adds a component aiming at fulfilling the constraints. For equality

constraints, this component involves some Lagrange multipliers which are computed at each step using

a Newton-like condition. The resulting algorithm is very simple and conceptually clear, and uses only

first order derivatives of the involved functions.

For inequality constraints, an active set strategy is proposed. Active constraints are essentially treated

as equality constraints. Activation is done when a constraint is violated (except for the case of many

similar constraints). Deactivation is decided on the basis of the sign of the corresponding Lagrange

multiplier.


Wednesday Sessions


4 - Optimization in additive manufacturing

Ismael Vaz, University of Minho, [email protected]

Co-author(s): Sérgio Pereira, University of Minho, [email protected]

Abstract

Additive manufacturing, also known as layered manufacturing and more recently synonymous of 3D

printing, has emerged in the last decades becoming an alternative to the traditional subtractive

manufacturing. Contrary from subtractive manufacturing which is a process where 3D objects are built

by cutting of material from a block of material, additive manufacturing is a process where 3D objects

are built by adding material in consecutive layers. This results in a process that will require less energy

consumption and waste of material. However, some limitations are pointed out to this process and in

particular to its four stages: part orientation, creation of supports, slicing and path planning. Both

orientation and supporting are usually related, since best orientation of the part to be built can result

in lower building time and less support needed, resulting in surface improvement. Slicing imply the

object division by layers leading to a staircase effect, being more evident for objects with high slopes

and curvatures. Path planning consists in the best nozzle path for layers building. An optimized path

planning will avoid the appearance of voids or excess of deposition material. This talk will address how

optimization can help additive manufacturing.

10:40 – 12:20

WA4 Multiobjective Optimization Contributed Session

Chair: Marta Pascoal Room: 6.2.48

1 - An integrated fuzzy c-means clustering and multi criteria decision

making methods for evaluating the logistic performance index: a

comparative analysis

Nimet Yapici Pehlivan, Selçuk University, Science Faculty, Department of Statistics, [email protected]

Abstract

Logistics and transportation play an important role in international trade relations. Logistics is defined

as a series of services and activities, such as transportation, warehousing, and brokerage, that help to

move goods and establish supply chains across and within borders. The overall aim of logistics is to

achieve high customer satisfaction through a high quality service with low or acceptable costs. Logistics

has added value by making products available in the right place and at the right time. The Logistics

Performance Index (LPI) measures logistics performance of countries according to six components:

customs, infrastructure, international shipments, logistics quality&competence, tracking&tracing and

timeliness. Logistics Performance Index has been published by the World Bank for the years of 2007,

2010, 2012, 2014 and 2016 with a report called “Connecting to Compete”. The Logistics Performance

Index has performed a worldwide online survey on respondents rated on a scale of 1(worst) to 5(best)

Wednesday Sessions


to assess logistics performance of the countries around the World. In the reports, the Logistics

Performance Index is generated from these six components using PCA which is a statistical method to

reduce the dimensionality of a dataset. Logistics Performance Index has helped policy makers for

improving their countries’ logistics performance.

Fuzzy C-Means algorithm introduced by James C. Bezdek (1981), is used in fuzzy system models to find

the membership values, which are assumed to represent optimum partitions of the given dataset.

Multiple criteria decision-making (MCDM) methods is a powerful tool widely used to evaluate

problems which contains multiple, usually conflicting criteria. A typical multi criteria decision making

problems involve three steps: i) Determination of the criteria and alternatives, ii) Evaluations of the

each criteria and alternatives, 3) Ranking of the alternatives.

In this study, we introduce an integrated Fuzzy C-means Clustering and Multi Criteria decision Making

method to evaluate the logistic performance index of countries with respect to the six components. At

first, countries are classified according to income levels by using Fuzzy C-means Clustering. Then, the

countries are ranked using Multi Criteria decision Making method. Finally, the ranking results of the

introduced method are compared with World Bank’s results.

2 - A fully fuzzy method for multi-objective fractional optimization

problems.

Rubi Arya, MNNIT Allahabad India, [email protected]

Co-author(s): Pitam Singh, MNNIT Allahabad India, [email protected]

Abstract

A fully fuzzy method is developed to solve multi-objective linear fractional (FFMOLF) programming

problem. The classical parameters of each objective function and constraint are represented by

approximate triangular fuzzy number. The problem is converted into a fully fuzzy multi-objective linear

fractional programming problem. A new method is developed to solve FFMOLFP on the basis of

lexicographic ordering and a theoretical result is also proposed for lexicographic optimal solution. The

efficiency of the method is measured by solving a numerical problem in efficient way.

3 - A new algorithm for the multiobjective minimum spanning tree

José Luís Santos, University of Coimbra, [email protected]

Co-author(s): Luigi Di Puglia Pugliese, University of Calabria, [email protected]; Francesca Guerriero,

University of Calabria, [email protected]

Abstract

A new approach to solve the multiobjective minimum spanning tree problem is presented. This

procedure is based on a label algorithm for the multiobjective shortest path problem in a transformed

network and works for any number of criteria. In this talk, it will be shown an example to explain how

the network is transformed. Finally, computational results will be reported that allow us to derive a

statistical model to predict the variation of the number of Pareto optimal solutions with the number of

nodes and criteria. Additionally, the computational results attest that our approach outperforms others

algorithms existing in the literature, namely the dynamic approach and two phase method.

Wednesday Sessions


4 - Bimaterial 3D printing: formulation and case study

Marta Pascoal, Department of Mathematics, University of Coimbra, [email protected]

Co-author(s): Daniel Bandeira, Department of Mathematics, University of Coimbra, [email protected]

Abstract

Three-dimensional printing is a process in which an object is created by adding successive layers of

material. In the stereolithography process the material is a liquid polymer, which is cured by a UV laser.

This method has grown in popularity when a single material is used. In the present work we consider its

extension to printing objects for which the polymer is cured around a metal grid. The metallic structure

causes additional difficulties, given that it can generate "shaded" areas in the polymer surface. We will

study the distribution of systems of galvanic mirrors along the walls of the printer, in order to reflect

the laser with well-chosen orientations and, thus, to reach the parts of the layer to solidify. These

systems will have fixed positions while printing, so that the stability of the equipment is maintained as

much as possible. However, they may be oriented to reflect the laser in the desired directions.

The goal of this work is to model mathematically the 3D bimaterial printing problem. The problem is

split into 2 parts. The first part consists of locating the minimum possible number of mirror systems

that ensure a full printing, called the Emitters Location Problem. The second is to assign each position

to be printed with one mirror system, thus obtaining the angles of incidence that are necessary to

proceed with the printing, called the Emitters Assignment Problem. The emitters location problem is

formulated as a set covering problem. The emitters assignment problem is formulated as a linear

program with both covering and assignment constraints. Two possible criteria are considered for this

problem: minimizing the number of mirror systems that is used for each printed layer, and maximizing

the incidence angles, looking for the minimization of the laser distortion when reaching the printing

layer. We describe two approaches for computing efficient solutions for the emitters assignment

problem.

Finally, the presented formulations and methods are tested and compared for a given case study. The

obtained results are reported and discussed in terms of the running times and of the quality of the

solutions.

10:40 – 12:20

WA5 Optimization in Engineering Contributed Session

Chair: Hideshi Ishida Room: 6.2.47

1 - Estimation of mature water flooding performance and optimization by

using capacitance resistive model and fractional flow model by layer

Luis Francisco Castillo Gamarra, Sinopec Argentina Exploration and Production, [email protected]

Co-author(s): Nestor Ramos, Sinopec Argentina Exploration and Production, [email protected];

Ignacio Borsani, Sinopec Argentina Exploration and Production, [email protected]

Abstract

Water flooding, the oldest and most common EOR method, increases the displacement efficiency in a

reservoir and also maintains the reservoir pressure for a long period of time for both onshore and

Wednesday Sessions


offshore fields. Water injection has proved to be the best method to enhance recovery from oil

reservoirs for project CM-123-A at Cañadón Minerales field, San Jorge Gulf Basin. Defining the

optimized injection rates and injection patterns, that depends on the geological structure of the

reservoir, is an essential operational and economical decision for reservoir management. In this paper,

the Multilayer Capacitance-Resistive Model (CRM), that takes into account implicitly the geological and

reservoir parameters, is used to find inter-well connectivity, optimize injection rates and with the

complement of net sand maps, petrophysical and production test data, check the consistency of the

solutions with all the available data to support the decisions. The CRM models receives the injection

rates variations as an input signal, of the different reservoirs, while the producer responses determine

the injector/producer pair connectivity quantitatively. The different runs of CRM can be used to detect

if some abrupt changes in the artificial lift of the producers affect the connectivity. Also this model is

used to predict gross production for individual reservoirs and with the coupling of a Fractional Flow

model we can estimate the oil production of each individual reservoir and detect the potential of the

different reservoirs and some improvements in the injection rates to optimize the oil production or

detect zones with low efficiency in the injection candidates to shut in due the high water cut of the

producers. The results show that the CRM approach has the capability to match the production history

and calibrate the dynamical effective parameters, and with this characterization optimize the injection

rates of the different wells injectors and reservoirs, during the immiscible flooding, understand water

injection movement, and as accessory the joint validation of the net sand maps. The CRM model was

able to detect inter-well connectivity for producers connected not only at fist line, but at second line,

with a clear response in field. All the Models were implemented in the framework of Optimization

Models and were solved with CONOPT4 of GAMS.

2 - Topology optimization to design magnetic circuits

Rtimi Youness, INPT (Polytechnic national institute of Toulouse), LAPLACE laboratory (Laboratory on plasma and conversion of energy), GREM3 group, Toulouse, France, [email protected]

Co-author(s): Frederic Messine, INPT (Polytechnic national institute of Toulouse), LAPLACE laboratory (Laboratory

on plasma and conversion of energy), GREM3 group, Toulouse, France, [email protected]

Abstract

For spatial plasmas thrusters, the propulsion of the ionized gas is provided thanks to a specific

electromagnetic field. Thus the efficiency of those thrusters is explicitly depending on the magnetic

topology inside. For this purpose magnetic circuits must be designed in order to supply more

complicated and demanding magnetic topologies. In our work we search for suitable layout materials

(magnetic circuits) and magnetic sources, that generates, with the lowest electromagnetic energy cost,

the required magnetic field. Consequently we solve an optimization problem that minimizes the energy

cost under a constraint which imposes the desired magnetic topology in a target domain. The cost and

constraints are function of electromagnetic quantities (field and potential vector…) which we compute

via Maxwells equations using finite elements software. In our case we simplify the 3D magneto static

problem to 2D-axisymmetric one, in order to speed up the computation time of magnetic quantities.

Concerning the optimization process, it is ran according to two kinds of control variables: the first one

consists of electromagnetic sources and the second one consists of the electromagnetic structure

topology. The electromagnetic sources are the current densities. Whereas the structure topology, It is

defined by the magnetic permeability space distribution. Indeed, we define a variable zone that we

mesh into small elements to which we assign magnetic permeability variables; Each element variable

Wednesday Sessions


can take either 1 for regions without ferromagnetic materials or a maximum value (defined by the

ferromagnetic materials used) otherwise. Thus the electromagnetic structure topology is represented

by binary variables, unlike electromagnetic sources ones that we take as reals. Thus, we developed a

SIMP based method to solve this mixed-integer optimization problem. This approach relaxes the binary

variables into real ones at first, and then penalizes non binary solutions using atan, as a penalization

function. For the cost function and the constraints derivative computations, we developed the adjoint

variable method (previously introduced for mechanical applications). This approach gives all gradient

components by using, only two times the finite element calculus. For the thruster topology

optimization, we developed a SIMP associated with an adjoint method based software, named ATOP. It

uses MATLAB fmincon routine and finite element method magnetics (FEMM) software. Numerical

experiments are being carried out on Hall effect thruster design application, and efficient results are

already obtained for structures with 2051 variables (3 are real variables and 2048 are binary ones).

3 - An interior point method-based solver for simulation of aircraft parts

riveting

Maria Stefanova, Department of Applied Mathematics, Peter the Great St.Petersburg Polytechnic University, St.Petersburg, Russia, [email protected]

Co-author(s): Sergey Lupuleac, Department of Applied Mathematics, Peter the Great St.Petersburg Polytechnic

University, St.Petersburg, Russia, [email protected]; Margarita Petukhova, Department of Applied Mathematics,

Peter the Great St.Petersburg Polytechnic University, St.Petersburg, Russia, [email protected]

Abstract

The simulation of the aircraft parts’ assembly process requires doing series of similar computations.

Arising contact problem should be solved in order to find stresses and gap between assembled parts

after fastening. Following the goal to speed up the computations we introduce primal-dual interior

point – based solver for convex quadratic programming problems arising from aircraft assembly. The

encouraging feature of this method is the polynomial worst-case time bound that is especially

important while solving large scale contact problems. The main challenges of the method are the

solution of ill-conditioned linear system of equations arising at each iteration as well as searching of

initial guess. We propose the algorithm for searching of feasible starting point based on physical

understanding of the problem. The searching algorithm is compared to the other existing approaches.

An effective preconditioner for conjugate gradient method is suggested as well. With regard to

application of interior point method to the contact problems, its working time is compared to dual

active set method.

4 - Non-parametric optimization of time-averaged quantities under small,

time-varying forcing: an application to a thermal convection field

Hideshi Ishida, Department of Mechanical Science and Bioengineering, Osaka University, Japan, [email protected]

Co-author(s): Chiharu Okema, Osaka University, [email protected]; Genta Kawahara, Osaka University,

[email protected]

Abstract

For any ordinary differential equation system (ODEs), Ishida et al. has shown that steady forced



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oscillations induced by small-amplitude, time-varying forcing can be expressed by an analytical solution

as long as a base state without forced vibration is stable, steady one [H. Ishida et al., Int. J. Heat Mass

Transfer, 55 (2012), 6618-6631]. It is a perturbation theory with a typical vibrational amplitude taken as

a perturbation parameter. Partial differential equation systems including Boussinesq-fluid system are

reduced to an ODEs by an appropriate discretization. Moreover, the base steady state is or is made to

be stable by the introduction of dumpers in many equipment, buildings, et al. That is the reason why

the theory is applicable to many practical problems of forced vibration. For example, the solution based

on the first-order corrections allows us to maximize the total amplitude of thermal convection field,

and Ishida et al. numerically confirmed that the optimal (resonance) state is actually the so-called

internal gravity wave resonance.

This study presents an optimization method of time-averaged quantities, i.e. direct-current

components, based on the theory with the second-order corrections [H. Ishida et al., Int. J. Heat Mass

Transfer, 96 (2016), 145-153]. The reduction of the optimization to an eigenvalue problem makes

possible a non-parametric optimization; the maximum (minimum) value and corresponding vibrational

form are respectively obtained by the maximum (minimum) eigenvalue and corresponding eigenvector.

As the first trial, it is applied to an optimization of time-averaged skin friction on a heated surface in a

two-dimensional square cavity. The minimized vibrational thermal field corresponds well to the internal

gravity wave resonance. On the other hand, the maximized field has stronger circulatory flow in any

vibrational phase, making the skin friction largest. It should be noted that not upper (lower) bound but

maximum (minimum) value is actually obtained by the non-parametric optimization in the sense that

the vibrational amplitude is infinitesimal.

13:50 – 15:05

WB1 Workshop Luís Gouveia Session II Workshop

Chair: Ángel Corberán Room: 3.2.14

1 - Layered graph approaches for the black-and-white traveling salesman

problem

Mario Ruthmair, University of Vienna, [email protected]

Co-author(s): Luís Gouveia, University of Lisbon, [email protected]; Markus Leitner, University of Vienna,

[email protected]

Abstract

We study modeling approaches based on layered graphs for the Black-and-White Traveling Salesman

Problem (Bourgeois et al., 2003) which asks for a Hamiltonian tour with minimal total costs subject to

additional constraints: The node set is partitioned into white and black nodes and the number of white

nodes between two consecutive black nodes in a feasible tour is limited (cardinality constraint).

Additionally, we have to ensure a distance constraint on the path between two consecutive black

nodes.

Two strategies to model the problem have been used in the literature. It is either modeled on the

original graph as traveling salesman problem using a single set of binary edge variables and with

additional non-trivial hop and distance constraints between every pair of black nodes (Ghiani et al.,

2006) or as a sequence of constrained paths composed of white nodes connecting pairs of black nodes

(Muter, 2015).


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In our work, we study and develop an intermediate approach based on the observation that it is

sufficient to guarantee the required distance (and hop) limit of the path from a given black node to the

next black node without explicitly stating which one it is. Thus, instead of stating the two constraints

(after adding appropriately defined variables) for each pair of black nodes, they are stated for each

black node only (that represents the source of each path). Based on this idea we develop several

variants of position- and distance-dependent reformulations together with corresponding layered

graph representations.

Branch-and-cut algorithms are developed for all proposed formulations which include extensive

preprocessing to reduce the size of the layered graphs and heuristics to obtain good primal bounds for

pruning the branch-and-bound tree.

We empirically compare all the proposed algorithms in an extensive computational study. The obtained

results allow us to provide insights into individual advantages and disadvantages of the different

layered graph models.

2 - Extending and projecting flow models for the (PC)ATSP

Pierre Pesneau, University of Bordeaux, [email protected]

Co-author(s): Luís Gouveia, University of Lisbon, [email protected]; Mario Ruthmair, University of Vienna,

[email protected]; Daniel Santos, University of Lisbon, [email protected]

Abstract

There are many ways of modelling the Asymmetric Traveling Salesman Problem (ATSP) and the related

Precedence Constrained ATSP (PCATSP). In this talk we present new formulations for the two problems

that can be viewed as resulting from combining precedence variable based formulations, with network

flow based formulations. As suggested in Gouveia and Pesneau (2006), the former class of formulations

permits to integrate linear ordering constraints. The motivating formulation for this work is a

complicated and "ugly" formulation that results from the separation of generalized subtour elimination

constraints presented in Gouveia and Pires (2001) (see also Gouveia and Pesneau (2006)). This so called

"ugly" formulation exhibits, however, one interesting feature, namely the "disjoint sub-paths" property

that is further explored to create more complicated formulations that combine two "disjoint path"

network flow based formulations and have a stronger linear programming bound. Some of these

stronger formulations are related to the ones presented for the PCATSP in Letchford and Salazar-

Gonzales (2016) and can be viewed as generalizations in the space of the precedence based variables.

Several sets of projected inequalities in the space of the arc and precedence variables and in the spirit

of many presented in [1] are obtained by projection from these network flow based formulations.

Computational results will be given for the PCATSP to evaluate the quality of the new inequalities.

3 - New decomposition approaches for the two-stage stochastic Steiner

tree problem

Ivana Ljubic, ESSEC Business School of Paris, France, [email protected]

Co-author(s): Markus Leitner, Martin Luipersbeck, Markus Sinnl

Abstract

New decomposition approaches for solving the two-stage stochastic Steiner tree problem with

complete recourse are proposed. These approaches are derived from a new ILP formulation (which is



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shown to be strongest among existing formulations) and are based on: dual ascent heuristic,

Lagrangian relaxation, and Benders decomposition.

The resulting method, which relies on an interplay of the dual information retrieved from the

respective dual procedures, computes upper and lower bounds and combines them with several rules

for fixing variables in order to decrease the size of problem instances.

The effectiveness of our method is compared in an extensive computational study with the state-of-

the-art exact approach, which employs a Benders decomposition based on two-stage branch-and-cut,

and a genetic algorithm introduced during the DIMACS Implementation Challenge on Steiner trees. Our

results indicate that the presented method significantly outperforms existing ones, both on benchmark

instances from literature, as well as on large-scale telecommunication networks.

4 - On the periodic mixed rural postman problem with irregular services

Ángel Corberán, University of Valencia, [email protected]

Co-author(s): Enrique Benavent, University of Valencia, [email protected]; Demetrio Laganà, Università della Calabria,

[email protected]; Francesca Vocaturo, Università della Calabria, [email protected]

Abstract

In this paper, we deal with an extension of the rural postman problem in which some links of a mixed

graph must be traversed once, or a specified number of times, over a given time horizon. These links

represent entities that must be serviced a specified number of times in some sub-periods of a given

time horizon. The aim is to design a set of least-cost tours, one for each period in the time horizon, that

satisfy the service requirements.

We refer to this problem as the periodic rural postman problem with irregular services (PRPPIS). Some

practical applications of the problem can be found in road maintenance operations and road network

surveillance.

In order to solve the PRPPIS, we propose a mathematical model and a branch-and-cut algorithm. In the

solution framework, constraints ensuring connectivity and other valid inequalities are identified by

using specific separation procedures. Some valid inequalities consider the particular nature of the

PRPPIS. We show the effectiveness of the solution approach through an extensive experimental phase.

13:50 – 15:05

WB2 Optimization-Based Control II: Algorithms and Applications Organized Session

Organizer/Chair: Fernando Fontes Room: 6.2.50

1 - Robust a priori planning to the dynamic and stochastic vehicle routing

problem

Marcella Bernardo, Bremer Institut für Produktion und Logistik, [email protected]

Co-author(s): Jürgen Pannek, Bremer Institute für Produktion und Logistik, [email protected]

Abstract

In the Dynamic and Stochastic Vehicle Routing Problem (DSVRP) a fleet of vehicles is routed to serve a

set of customers at minimum cost in the presence of dynamic events. The DSVRP seeks to handle and


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respond to all dynamic events as well as exploit stochastic information in an ongoing fashion. First, an

optimization is performed based on a priori information, computing the so-called a priori route. Then,

when an event occurs during the execution of the a priori route, the route is adapted to accommodate

for the changes. One way to address the problem is to optimize online each time an event occurs, in

order to determine the best course of action. However, if the rate of events is high, this approach may

not be real time capable. We propose a Robust Solution Approach to the Capacitated DSVRP, where

demand and travel times are dynamic and stochastic. The idea is to design a robust a priori route that

allows to accommodate new events without losing structural properties and optimality. Future

scenarios are generated and optimized, but just once at the beginning of the planning horizon, using

Monte Carlo Simulation and Simulated Annealing. To estimate the range of tolerable events, we

compare the plans for each scenario to a plan for demand and travel time average, and incorporate the

difference into cost function. The effectiveness of the approach is evaluated using a benchmark

datasets. Based on Fiacco’s theorem, we conjecture that the robust a priori route is still optimal.

2 - Driving an autonomous car using MPC

Matthias Knauer, Universität Bremen, [email protected]

Co-author(s): Christof Büskens, Universität Bremen, [email protected]

Abstract

Scientists from mathematics and informatics at the Universität Bremen work together to bring their

results from autonomous trajectory planning for unmanned space exploration down to earth in the

project AO-Car: they implement controls based on optimization and neuroinformatics to provide a new

approach in the field of autonomous driving. Optimal control problems require the complete

information of constraints or objectives in the current task in advance. Due to nonlinearities in the

model of the car, the usage of feedback controls causes difficulties, which could be handled for

example by linearizing the system or by using adaptive controllers. As the current state of information

changes rapidly in car driving maneuvers, we propose a model predictive control algorithm based on

the repeated solution of optimal control problems on limited time horizons. By using our transcription

method TransWORHP to solve optimal control problems, which is based on the ESA NLP solver WORHP,

we can exploit sparsity structures in the problem formulation for real-time capable results. Numerical

results for some first maneuvers implemented on a car with open interfaces to sensors and motors will

be shown. Finally, some aspects on reducing the calculation time will be discussed.

3 - An adaptive mesh refinement algorithm with time–dependent criteria

for model predictive control

Luís Tiago Paiva, SYSTEC–ISR, Universidade do Porto, Portugal, and ISEP, Instituto Politécnico do Porto, Portugal, [email protected]

Co-author(s): Fernando A.C.C. Fontes, Universidade do Porto, Portugal, [email protected], SYSTEC–ISR

Abstract

We address a sampled–data nonlinear Model Predictive Control (MPC) scheme, the efficiency of which

is mainly determined by the efficiency in solving the underlying continuous–time optimal control

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problems (OCP). When solving nonlinear continuous-time OCPs numerically, the selection of the

number of nodes for the time–mesh as well as their location are key factors affecting the overall

computational time and the accuracy of the solution.

We develop efficient algorithms, based on refining the time-mesh adaptively, to numerically solve

nonlinear optimal control problems with pathwise state constraints. Such algorithms generate time–

meshes addressing two main issues: on the one hand, the number of discretization points of the time–

mesh, which is a crucial factor determining the computational time and, on the other hand, the

location of these points along the time domain which has a major impact in the accuracy of the

solution. The proposed algorithm provides local mesh resolution considering a time–dependent

refinement criterion, and enables a higher accuracy in the initial part of the receding horizon.

The adaptive mesh strategy leads to results which are as accurate as the ones given by a fine

equidistant–spaced mesh and as fast as the ones given by a coarse equidistant–spaced mesh.

13:50 – 15:05

WB3 Nonlinear Optimization

Organized Session

Organizer/Chair: Benoît Pawuels Room: 6.2.49

1 - A line-search algorithm inspired by the adaptive cubic regularization

framework, with a worst-case complexity 23/( εO )

El Houcine Bergou, INRA, [email protected]

Abstract

Adaptive regularized framework using cubics (ARC) has recently emerged as a new alternative to line-

search and trust-region for smooth nonconvex optimization, with an optimal complexity amongst

second-order methods. In this work, we propose and analyze the use of a special scaled norm in ARC of

the form MxTxxM , where M is a given symmetric positive definite matrix that satisfies a

specific secant equation. We show, using this norm, collinearity relation between the trial step of ARC

and the Newton step. In this case, ARC behaves as a line-search algorithm along the Newton direction,

with a special backtracking strategy and acceptability condition. Under appropriate assumptions, the

new algorithm converges globally and has the same worst-case complexity as ARC. Furthermore, we

have proposed similar analysis when considering trust-region framework. The good potential of the

new line-search algorithms is showed on a set of optimization problems.

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2 - Robust inversion for functional inputs

Mohamed Reda El Amri, IFP Energies Nouvelles/Université Grenoble-Alpes, [email protected]

Co-author(s): Clémentine Prieur, Université Grenoble-Alpes, [email protected];

Céline Helbert, École Centrale de Lyon/Institut Camille Jordan/Université de Lyon, [email protected];

Delphine Sinoquet, IFP Energies Nouvelles, [email protected]; Olivier Lepreux, IFP Energies nouvelles,

[email protected]

Abstract

This talk is concerned with the commonly occurring situation in which we have an expensive-to-

evaluate function f which takes two types of input variables: a set of ”design” (control) variables, xc ,

and a set of ”environmental” variables, xe , which are not controllable and can be scalar or functional,

while scalar random variables are governed by some known distributions, the probability distributions

of functional variables are known only from an available samples of realizations. Different methods

exist to model uncertainties associated with functional random variables: we can assume that

functional variables have a finite support, then the probability distribution is approached by a discrete

one; or we decompose the functional variables on a functional basis, and model, by a Gaussian mixture

for example, the joint probability density function of the coefficients selected in the decomposition.

We study the problem of estimating the set of controlled variables that leads a system to satisfy

constraints like reliability constraints or environment constraints (e.g. output smaller than a threshold

T ). The goal is to identify the set Txexcfxexc ,,, , by taking the expectation over the

distribution of the environmental variables. The problem becomes then deterministic, i.e. it will

depend only on the controlled variables, and it aims to identify the set

TxexcfExcgxcE ,)(, .

We review a method that considers a Gaussian process model trained on few evaluations of the

expensive-to-evaluate function and sequentially selects new evaluations in order to reduce uncertainty

on the estimation of . The proposed strategy is first tested on test cases, and then applied to a real-

life automotive application motivating the use of such strategies for robust inversion.

3 - New multi-disciplinary optimization (MDO) approaches based on

domain decomposition

Benoît Pauwels, IRT Saint Exupéry, [email protected]

Co-author(s): Serge Gratton, IRIT-ENSEEIHT, [email protected]; Zaikun Zhang, Hong Kong Polytechnic

University, [email protected]

Abstract

First, we present an optimization framework with space decomposition for constrained problems. In

this iterative approach the optimization variables are decomposed into possibly overlapping subsets.

Restrictions of the objective function to these subsets are then viewed as cost functions of sub-

problems of the original problem. The sub-problems are solved concurrently in a trust region, yielding

quantities that are then linearly combined so as to define a trial iterate, that is accepted or not

depending on the ratio of the sum of the sub-models decreases over the objective function decrease,

similarly to the traditional step acceptance mechanism of globalized methods. The global convergence


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of this framework is proved for bound-constrained problems.

Second, we describe how this theoretical framework can be applied to MDO. We show that this space

decomposition framework is well-suited for multi-disciplinary optimization problems that arise in

aircraft design for instance, thanks to the introduction of a mechanism for handling the coupling

variables of MDO. For such problems the computation of an objective function value requires the

evaluation of so-called `disciplines’, each depending on a subset of the design variables (some of which

are shared by all disciplines). We present promising results obtained with our method in comparison

with classical formulations of multi-disciplinary optimization problems, such as Multi-Disciplinary

Feasible (MDF) and Individual Discipline Feasible (IDF).

N.B. This work is based on a paper in preparation: "Optimization by Space Transformation" by S.

Gratton, L. N. Vicente and Z. Zhang.

13:50 – 15:05

WB4 Production Scheduling

Contributed Session

Chair: João Basto Room: 6.2.48

1 - A scheduling problem and node weighted coloring problem

Yash Aneja, University of Windsor, [email protected]

Co-author(s): Xiangyong Li, Tongji University, Shanghai, China, [email protected]; R. Chandrasekaran,

[email protected]

Abstract

We consider a scheduling problem, where several jobs, each with a processing time, are given. Certain

pairs of jobs cannot be done simultaneously. The objective is to minimize the makespan. We show

relationship of this problem to the Weighted Vertex Coloring Problem (WVCP). Exploiting structure of

our formulation, we present a Benders decomposition approach to solve this problem. We present

computational results to demonstrate the advantage of our approach for solving the WVCP in

comparison with approaches existing in the literature.

2 - Simultaneously scheduling production, transportation and storage in

flexible manufacturing systems

Seyed Mahdi Homayouni, LIAAD- INESC TEC, Universidade do Porto, Porto, Portugal, and Department of Industrial Engineering, Lenjan Branch, Islamic Azad University, Esfahan, Iran, [email protected]

Co-author(s): Dalila B.M.M. Fontes, LIAAD-INESC TEC and FEP, Universidade do Porto, Porto, Portugal,

[email protected]

Abstract

This work proposes a mixed integer linear programming model for the simultaneous scheduling of

production, transportation, and storage tasks in flexible manufacturing environments.

Flexible manufacturing systems (FMS) comprise computer numerical control (CNC) machines,


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automated guided vehicles (AGVs), and automated storage/ retrieval systems (AS/RS). Performance of

such a sophisticated production system is highly dependent on the optimal performance of its main

components. The problem of scheduling simultaneously production, transportation, and storage is

addressed in this work. Production scheduling refers to sequencing operations on CNC machines;

transportation scheduling refers assigning to AGVs the transportation of jobs between machines; and

storage scheduling refers to sequencing jobs retrieval and storage in the AS/RS.

The production scheduling in FMS environment is highly dependent on the availability of the jobs at

load/unload (LU) station and their transportation to the machine. To the best of knowledge, Jerald et

al. (2008) and Gnanavel Babu et al. (2009) are the only researchers that consider scheduling

simultaneously production, transportation, and storage operations in FMS. Both researches considered

minimizing penalty cost, minimizing machine idle time, and minimizing the distance travelled by the

storage/ retrieval (S/R) machine. Jerald et al. (2008) proposed several metaheuristic algorithms such as,

genetic algorithm, particle swarm intelligence, and sheep flock heredity algorithm and Gnanavel Babu

et al. (2009) proposed artificial immune system algorithm for this problem. However, no optimal

solution methods are known for such problems. Here a mixed integer linear programming model is

proposed, with which optimal solutions can be found. This is important as a mean to develop

alternative heuristics and to be able to provide a quality measure for the solutions found by the

(meta)heuristic, at least for smaller instances.

Acknowledgments: We acknowledge the financial support of "NORTE-01-0145-FEDER-000020",

financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL

2020 Partnership Agreement.

3 - Sequencing of production lines in the footwear industry

João Basto, INESC TEC, [email protected]

Co-author(s): José Soeiro Ferreira, INESC TEC, [email protected]; Rui Diogo Rebelo, [email protected]

Abstract

In the last years, the paradigm of the Portuguese footwear industry has improved drastically, to

become one of the main world players. In fact, a lot has changed, from low-cost mass production to

serving clients consisting of small retail chains, where orders are small and models are varied. In order

to progress and deal with such modifications, the footwear industry started investing in creative design,

technological and management leadership solutions and skilled labour, among other aspects.

The industrial case presented in this paper fits that purpose. The goal is to contribute to the solution of

complex sequencing problems arising in the new mixed-model flexible automatic stitching lines of an

important footwear factory.

The project starts by building an optimisation model, which has been validated, and various results

have been obtained. Although the model has its own usefulness, the CPLEX program is only capable of

reaching optimal solutions for small problem instances. Therefore, a recent metaheuristic, the

Imperialist Competitive Algorithm (ICA), has been chosen to tackle larger problems. After the

indispensable adaptation to the real sequencing case, the ICA is capable of finding optimal results for

smaller instances and to achieve adequate solutions for real problems in short periods of time.

Moreover, the analysis of the computational results makes it possible to conclude that the

implementation of the ICA improves the results obtained so far by the method currently used in the

factory. Moreover, it is also possible to make relevant proposals for possible improvements in the

balancing of the lines.


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13:50 – 15:05

WB5 Equilibrium and Complementarity

Contributed Session

Chair: Andreas Fischer Room: 6.2.47

1 - A block active set algorithm for fractional quadratic programming on

the unit simplex and for the symmetric eigenvalue complementarity

problem

Klaus Schönefeld, TU Dresden, [email protected]

Co-author(s): Carmo P. Brás, Universidade Nova de Lisboa, [email protected]; Andreas Fischer, TU Dresden, Andreas.Fischer@tu-

dresden.de; Joaquim J. Júdice, Instituto de Telecomunicações, Coimbra, [email protected]; Sarah Seifert, TU Dresden,

[email protected]

Abstract

To solve the symmetric eigenvalue complementarity problem (EiCP), an existing equivalent

reformulation is treated. It consists in finding a stationary point of a fractional quadratic program on

the unit simplex. The spectral projected-gradient (SPG) method has been recommended to this

optimization problem when the dimension of the symmetric EiCP is large and the accuracy of the

solution is not a very important issue. A new algorithm is presented. It combines elements from the

SPG method and the block active set method, where the latter was originally designed for box

constrained quadratic programs. The SPG method projects onto the unit simplex. In the new algorithm,

the much cheaper projection onto the nonnegative orthant is used instead. This can be of particular

advantage for large and sparse symmetric EiCPs. Global convergence to a solution of the symmetric

EiCP is established. Computational experience with medium and large symmetric EiCPs confirms the

expected properties of the new algorithm.

2 - Newton-type methods for Fritz John systems of generalized Nash

equilibrium problems

Andreas Fischer, TU Dresden, [email protected]

Co-author(s): Markus Herrich, TU Dresden, [email protected]

Abstract

A well-known approach for solving a generalized Nash equilibrium problem (GNEP) is to consider a

necessary optimality condition and to reformulate it as a nonsmooth system of equations. Frequently,

the Karush-Kuhn-Tucker (KKT) conditions of all players are concatenated. It was shown by Dorsch,

Jongen, and Shikhman that, due to the lack of a suitable constraint qualification, solutions of a GNEP

may exist that do not satisfy the KKT but the Fritz John (FJ) conditions. The corresponding nonsmooth

system of equations is (similar to the KKT case) underdetermined since we assume that there are

constraints shared by all players. We show that some Newton-type methods recently developed for

certain constrained systems of nonsmooth equations can be successfully applied to a nonsmooth

system that is equivalent to the FJ conditions for GNEPs. In particular, we provide conditions for local

quadratic convergence which are weaker than existing ones.





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16:45 – 18:00

WC1 Workshop Luís Gouveia Session III

Workshop

Chair: Bernard Fortz Room: 3.2.14

1 - The network design problem with vulnerability constraints

Markus Leitner, University of Vienna, Vienna, Austria, [email protected]

Co-author(s): Luís Gouveia, Universidade de Lisboa, Lisbon, Portugal, [email protected]; Martim Joyce-Moniz,

GERAD and Polytechnique Montreal, Montreal, Canada, [email protected]

Abstract

The aim of the network design problem with vulnerability constraints is to design survivable

telecommunications networks that impose length bounds on the communication paths of each

commodity pair, before and after link failures. We first observe that this problem is not equivalent to

designing a network containing a number of length-bounded disjoint paths between each relevant

node pair, i.e., the hop-constrained survivable network design problem for which different integer

programming formulations and solution algorithms have been proposed. The reason for this is that

Mengerian-like theorems do not hold for paths with hop constraints, i.e., designing a network including

k edge disjoint paths with at most H hops between two nodes is not equivalent to designing a

network guaranteeing the existence of a path with at most H hops between them after the failure of

1k edges. Besides showing that the solutions of the two related problems can be different, we

propose integer programming formulations for the case of a single failure that are based on different

graph-theoretical characterizations of feasible solutions. In addition to these compact formulations, we

also introduce and discuss three branch-and-cut methods which are significantly more efficient in

solving the network design problem with vulnerability constraints. We present the results from our

extensive computational study in which we also analyze whether the solutions obtained from solving

the network design problem with vulnerability constraints are really different from those obtained from

considering the classical hop-constrained survivable network design problem

2 - Maximization of protected demand in telecommunication networks

using partial disjoint paths

Amaro de Sousa, Instituto de Telecomunicações, Universidade de Aveiro, [email protected]

Co-author(s): Luís Gouveia, DEIO, Faculdade de Ciências, Universidade de Lisboa, [email protected]; Pedro

Patricio, Departamento de Matemática, Universidade da Beira Interior, [email protected]

Abstract

In this presentation, we address the problem of maximizing the total protected demand of a set of

commodities that must be routed on a given capacitated network. We define the protected demand of

a given routing solution as the total demand that is protected, on average, when a single link fails. We

consider protection based on 2 routing paths per commodity and we address three types of path

protection: 1+1, 1:1 and 1:1 with preemption. The aim is to find a routing solution, which includes the

decision on whose commodities are to be protected, that maximizes the total protected demand. For

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each type, we discuss how the associated optimization problem can be modelled, through integer

linear programming (ILP), both in the traditional case where each pair of routing paths assigned to a

commodity must be link disjoint and in the case where each pair of routing paths can be partial link

disjoint. We present computational results testing the scalability of the ILP models. Based on the

computational results, we show that the use of partial disjoint protection paths enables to protect

more demand for the same network resources on all three types of path protection.

3 - Combining discretization and Dantzig-Wolfe reformulations: the case

of the fixed-charge transportation problem

Bernard Gendron, CIRRELT and DIRO, Université de Montréal, [email protected]

Abstract

Discretization is a well-known reformulation technique for mixed-integer linear programming (MILP)

models, most notably applied to optimization problems in graphs and networks. The technique involves

the introduction of a potentially large number of binary variables indexed by a discrete set, thus

allowing the addition of valid inequalities that improve the linear programming (LP) relaxation bound.

Deriving equivalent valid inequalities in the space of original variables is in general difficult. It is

interesting to note, however, that the very same valid inequalities can be rewritten directly in Dantzig-

Wolfe reformulations derived from the primal interpretation of Lagrangian relaxations. The

combination of valid inequalities derived from discretization and Dantzig-Wolfe reformulations yields

LP relaxation bounds that improve upon both the discretized model LP bound and the Lagrangian dual

bound. We illustrate this result on the fixed-charge transportation problem, providing a novel

theoretical interpretation to a recently proposed branch-and-price-and-cut algorithm for this problem.

4 - Connectivity and hop constraints in a social graph

Bernard Fortz, Université Libre de Bruxelles, [email protected]

Abstract

The talk will give some fresh light on some classical results in network design, and in particular

problems involving network connectivity and hop constraints. We consider design and routing

problems in a rooted social graph with root node LG. To make the graph reliable, we impose multiple

paths with a hop limit from the root node to every other node in the graph. We show that the best

relaxations of the problem are obtained using layered graphs, where layers correspond to different

activities in the social graph and where the root node plays a very important role.

Wednesday Sessions


16:45 – 18:00

WC2 Variational Inequalities and PDE-Constrained Optimization I

Organized Session

Organizer/Chair: Livia Susu Room: 6.2.50

1 - On subdifferentials of PDE solution operators

Constantin Christof, TU Dortmund, Germany, [email protected]

Co-author(s): Christian Clason, University of Duisburg-Essen, Germany, [email protected];

Christian Meyer, TU Dortmund, Germany, [email protected]

Abstract

This talk is concerned with the optimal control of a semilinear partial differential equation that involves

a non-differentiable Nemytskii operator. We characterize the Bouligand subdifferential (or, more

precisely, the Bouligand subdifferentials) of the control-to-state mapping of the considered optimal

control problem completely and use the resulting characterization to derive a necessary optimality

condition that is stronger than Clarke stationarity. Our results allow us to identify Qi's subdifferential

with an appropriately defined Bouligand subdifferential and, moreover, demonstrate that the behavior

of an optimal control problem governed by a non-smooth partial differential equation changes

significantly when the problem is discretized. We conclude the talk with some numerical examples.

2 - Optimal control of the wave equation with BV-functions

Sebastian Engel, University of Graz, Austria, [email protected]

Co-author(s): Karl Kunisch, University of Graz, Austria, [email protected]; Philip Trautmann, University of Graz, Austria,

[email protected]

Abstract

An optimal control problem associated to the undamped linear wave equation with time depending

controls of bounded variations (BV), multiplied by fixed space depending shape functions with pairwise

disjoint supports is considered. More precisely the problem under consideration is given by the least

squares distance of a desired state to the controlled wave equation in the space time L2-Norm and an

additional total variation term of the derivative of the BV-function controls.

Using the total variation of a BV-function in the mentioned cost functional causes sparsity in the

derivative of the optimal control (Dirac measures) enhances locally constant controls with jumps at the

corresponding Diracs.

This sparsity property can be partially represented with the necessary and sufficient first order

optimality condition.

Numerically we employ a L2 regularization of the weak derivative of the controls times a constant

gamma, which we later take to 0. As a consequence the optimal controls live in the Sobolev space H1.

One is then able to show that the BV optimal controls can be approximated in the BV weak* topology

with the unique optimal H1 controls for gamma going to 0. The main purpose of this regularization is to

use the semi-smooth Newton algorithm.

In the full discretized problem, we consider a three level finite element method for the weak

formulation of the wave equation with linear continuous finite elements in time and space. The


Wednesday Sessions


controls "u" can be identified with there unique decomposition into a L2 function "dt u" and a constant

"u(0)", representing the derivative of the control and the initial value at time 0. In the full discretized

problem we discretize "dt u" by linear continuous finite elements.

Finally we apply the semi-smooth Newton algorithm to approximate our H1 controls and a BV-path

following algorithm to get an approximation of the BV optimal controls with respect to the time-space

discretization refinement level.

3 - Optimal control of nonsmooth, semilinear parabolic equations

Livia Susu, University of Duisburg-Essen, Germany, [email protected]

Co-author(s): Christian Meyer, TU Dortmund, Germany, [email protected]

Abstract

This talk is concerned with an optimal control problem governed by a semilinear, nonsmooth operator

differential equation. The nonlinearity is locally Lipschitz-continuous and directionally differentiable,

but not Gâteaux-differentiable. By employing the limited differentiability properties of the control-to-

state map, first-order necessary optimality conditions in qualified form are established, which are

equivalent to the purely primal condition saying that the directional derivative of the reduced objective

in feasible directions is non-negative. The talk ends with the application of the general results to a

semilinear heat equation.

16:45 – 18:00

WC3 Continuous Optimization Contributed Session

Chair: Rohollah Garmanjani Room: 6.2.49

1 - A gradient sampling method on algebraic varieties

Seyedehsomayeh Hosseini, University of Bonn, [email protected]

Co-author(s): Andre Uschmajew, University of Bonn, [email protected]

Abstract

This talk is concerned with the numerical solution of nonsmooth optimization problems on real

algebraic varieties. The method proposed in this work generalizes the gradient sampling method for

Riemannian manifolds to problems on such sets. Our motivation comes from applications in low-rank

matrix and tensor optimization, where one is faced with the fact that smooth manifolds of fixed rank,

say, manifolds of rank- r matrices, are not closed, and hence convergence of Riemannian algorithms is

difficult to establish even for smooth functions.


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2 - The new diagonal Hessian approximation of multi-step gradient -type

methods for large scale optimization

Mahboubeh Farid, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark, [email protected]

Co-author(s): Henrik Madsen, Department of Applied Mathematics and Computer Science, Technical University of

Denmark, Lyngby, Denmark, [email protected]

Abstract

This paper emphasizes on developing gradient-type methods for minimizing large scale unconstrained

optimization problems by employing the three-step method to improve the diagonal approximation of

Hessian in each step. The diagonal Hessian approximation is constrained to satisfy the generalized weak

secant equation by employing interpolating curves. The interesting feature of this approach is that we

use the information of three most recent steps which are determined by interpolating polynomial

forms to update the current diagonal approximation of Hessian instead of using the information one

previous step. The fixed-point approach is used for estimation of the parameter value in the

interpolating curve. The global convergence of the proposed method is approved. The efficiency of the

proposed method is evaluated with other variants of multi-step gradient-type methods.

3 - Worst-case complexity analysis of convex nonlinear programming

Rohollah Garmanjani, University of Coimbra, Portugal, [email protected]

Abstract

We will review recent studies on the worst-case complexity analysis of some derivative-based and

derivative-free optimization algorithms. Within convex smooth unconstrained setting, we present a

unified framework for worst-case complexity analysis of both first and second-order methods when

deriving the size of the gradient below some given threshold is desired. We then present a derivative-

free algorithm along with its complexity analysis for the minimization of nonsmooth convex

constrained problems.

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16:45 – 18:00

WC4 Railway Optimization

Contributed Session

Chair: António Antunes Room: 6.2.48

1 - Scheduling gantry cranes with transshipment trucks in rail-road

container terminals

Peng Guo, School of Mechanical Engineering, Southwest Jiaotong University, [email protected]

Co-author(s): Wenming Cheng, School of Mechanical Engineering, Southwest Jiaotong University,

[email protected]; Yi Wang, Department of Mathematics and Computer Science, Auburn University at

Montgomery, [email protected]

Abstract

Modern hinterland rail-road container terminals serve as main transferring nodes in hub-spoke

networks and enable a rapid consolidation of containers between freight trains and trucks. The

operational performance of gantry cranes directly influences the productivity of the transshipment.

An important decision problem during the container processing operations of railway terminals is the

scheduling of gantry cranes, which assigns these transferring operations to the cranes and decides the

optimal sequence of these operations on each crane. For ensuring the servicing quality of external

trucks, the loading/discharging operations of external trucks are considered separately when

scheduling these cranes. A mixed integer programming model is proposed for the resulting problem.

Meanwhile an efficient and easily adaptable heuristic based on Fix-and-Optimize is presented. The

proposed algorithm involves four neighborhood operators for formulating the subproblem and the

resulted subproblem is solved by a general solver. It runs in successive intervals which change the

behavior of operators and compute their own statistics to adapt selection probabilities of operators.

Finally, 600 randomly generated instances are used to test the performance of the proposed algorithm.

Based on the computational results, the proposed heuristic significantly outperforms the solver, in

terms of solution quality and run time.

2 - An evolutionary optimization model for solving large-scale line

planning problems in railways

Carlos Iglésias, SISCOG – Sistemas Cognitivos, S. A., [email protected]

Co-author(s): Ana Sofia Carvalho, SISCOG – Sistemas Cognitivos, S. A., [email protected]; Susana Brandão,

SISCOG – Sistemas Cognitivos, S. A., [email protected]; Ricardo L. Saldanha, SISCOG – Sistemas Cognitivos,

S. A., [email protected]

Abstract

The main planning stages of a public transportation system include demand estimation, line planning,

timetabling, rolling stock scheduling and crew scheduling. This work focus on the line planning process

which is fundamental since many networks are reaching saturation and require new approaches to

reduce operational costs and increase service quality with the available resources.



Wednesday Sessions


In the railway context, line planning (LP) consists in the selection of service lines and frequencies. These

lines and frequencies are, then, used to create a timetable which should satisfy the passengers’ needs.

The goal is to cover a given travel demand, at a minimum operational cost while maintaining some level

of passenger satisfaction. The travel demand is known for origin-destination stations in the so-called

OD matrix that does not have information about routes used by passengers while travelling between

origin and destination. This raises difficulties when estimating how many passengers flow on each line

as it depends on all alternative routes available to passengers. Thus, passenger flows, lines and

frequencies, while not linearly related, must be jointly estimated to obtain robust results.

We introduce a new approach to estimate lines’ passenger flows by introducing a limited pool of

preferential routes between origin-destination stations that passengers choose based on personal

preference or schedule. These possible routes are independent of the set of service lines in the LP

solution. Given the final set of selected service lines, our algorithm estimates the exact sequence of

lines, and transfer stations, used by passengers to transverse their preferred route. Another important

contribution of this work is the introduction of a fleet size constraint which leads to further

entanglement between passenger flow estimation and line’s capacity.

We solve the LP problem using a biased random key genetic algorithm, for which we developed

specialized crossover and mutation operators.

We show compelling results for a major European railway operator comparing scenarios that favor

passengers’ satisfaction with scenarios more focused on the operations’ level.

3 - Revenue management in a railway company: a case study in Portugal

António Antunes, University of Coimbra, [email protected]

Co-author(s): Joana Castro, CP - Comboios de Portugal, [email protected]; Joana Cavadas, University of Coimbra,

[email protected]

Abstract

Revenue management is the collection of strategies and tactics that companies use to scientifically

manage the demand for their products, and involves three types of decisions: structural, e.g., which

selling format and segmentation mechanisms to use; quantity-based, e.g., how to allocate output or

capacity to different segments or products; and price-based, e.g., how to set posted prices, individual-

offer prices, and reserve prices.

In this paper, our focus is on quantity-based revenue management in the railway industry. Specifically,

we describe a study carried out to assess the possible gains that CP, the leading passenger railway

company in Portugal, could make by applying quantity-based revenue management techniques in their

ticket sales for Alfa trains instead of the current first-come-first-served (FCFS) policy.

The reason for the possible gains is because, when CP sells a ticket for a short trip, say Aveiro-Porto, for

which the current fare is 19.7 Euros in comfort class, if the sale corresponds to the last seat available in

that class, then it cannot be sold for a long trip, say Lisbon-Oporto, by 42.4 Euros. This signifies a

(maximum) loss of 22.7 Euros on a single seat. The first component of the study consisted in the

estimation of the trip demand for each origin-destination (OD) pair and fare class based on the ticket

sales information available at CP. This estimation was not straightforward because trip demand is only

observed when classes are not sold out; that is, the demand distribution is censored from above.

Moreover, it has been necessary to take into account fare class transfer effects.

The development of a network capacity control optimization model to calculate the booking limits to

apply to each OD pair and class was the second component of the study. We have worked with a


Wednesday Sessions


deterministic model, considering the mean trip demand for each OD-pair (and neglecting the variability

of trip demand around the mean). In the future, we plan to test a stochastic model.

Finally, the third component of the study was the application of the model to the Alfa trains,

considering the different seasons of the year and days of the week, and the evaluation of the additional

revenues that CP could expect from applying booking limits to ticket sales.

The results we have obtained so far provide clear indications on the advantages, but also on the

limitations, of the revenue management technique under assessment.

Thursday Sessions


Thursday

10:40 – 12:20

TA1 Facility Location with Applications Organized Session

Organizer/Chair: Francisco Saldanha-da-Gama Room: 6.2.50

1 - A stochastic formulation for the simple plant location problem with

order

Xavier Cabezas, The University of Edinburgh, [email protected]

Co-author(s): Sergio García, The University of Edinburgh, [email protected]

Abstract

The simple plant location problem with order (SPLPO) is a variant of the simple plant location problem

(SPLP) where the customers have preferences on the facilities that will serve them. The problem can be

modeled as a mixed integer linear program (MILP) and some results about its strength can be found in

the literature. In this paper we present a more general MILP formulation where different preferences

are considered for a group of scenarios. This leads to a model whose structure is analyzed in detail.

Furthermore, we study possible methods that exploit this structure and that could be used to solve the

problem.

2 - Outer approximation and submodular cuts for maximum capture

facility location problems with random utilities

Ivana Ljubic, ESSEC Business School of Paris, France, [email protected]

Co-author(s): Eduardo Moreno, Faculty of Engineering and Sciences, Universidad Adolfo Ibanez, Santiago, Chile,

[email protected]

Abstract

We consider a family of competitive facility location problems in which a “newcomer” company enters

the market and has to decide where to open a set of new facilities so as to maximize its market share.

The multinomial logit model is used to estimate the captured customer demand. We propose a first

branch-and-cut method for this family of difficult mixed-integer non-linear problems. Our algorithm

combines two types of cutting planes that exploit particular properties of the objective function: the

first one are the outer-approximation cuts and the second one are the submodular cuts. The algorithm

is computationally evaluated on three datasets from the recent literature. The obtained results show

that our new exact approach drastically outperforms state-of-the-art methods, both in terms of the

computing times, and in terms of the number of instances solved to optimality.

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3 - Supply chain complexity and the network design: location does matter!

Mozart B.C. Menezes, Supply Chain Department, Kedge Business School, Talence, France, [email protected]

Co-author(s): Diego Ruiz-Hernández, Department of Quantitative Methods, University College for Financial Studies

– CUNEF, Madrid, Spain, [email protected]

Abstract

Facility location problems are well known problems in the combinatorial optimization field of study,

where the objective is to minimize the cost incurred to serve customer from a set of facilities. Our

study brings to the field of facility location the concept of operations complexity, opening up a new

research line within the field. When defining facility location one should aim not only to reduce

operational costs but also to keep complexity in what we call complexity comfort range in which tactical

and operational decisions are at their bests. The preliminary (empirical) results suggest that ignoring

complexity issues may hurt that same bottom line that the locational problem is trying to improve.

4 - Service location for unit demand customers: dealing with uncertainty

Francisco Saldanha-da-Gama, Universidade de Lisboa, Faculdade de Ciências, Department of Estatística e Investigação Operacional e Centro de Matemática, Aplicações Fundamentais e Investigação Operacional, Lisboa, Portugal, [email protected]

Co-author(s): Maria Albareda-Sambola, Universitat Politècnica de Catalunya, Dept. d'Estadística i Investigació

Operativa, Terrassa, Spain, [email protected]; Elena Fernández, Universitat Politècnica de Catalunya, Dept.

d'Estadística i Investigació Operativa, Barcelona, Spain, [email protected]

Abstract

In this work, we study the so-called facility location problem with Bernoulli demands (FLPBD). A finite

set of potential locations for the facilities is given. The demand of each customer follows a Bernoulli

distribution with a probability that may change from customer to customer. The facilities are

capacitated in terms of the number of customers they can serve.

The FLPBD can be looked at as a two-stage stochastic discrete facility location problem such that a

here-and-now decision must be made concerning the facilities to open and the (single) allocation of the

customers to the opened facilities. Since this decision is made prior to knowing which customers will

eventually call for being served, it may happen that for one or several opened facilities the above

allocation results in a cluster of customers with cardinality larger than the capacity of the facility.

Accordingly, after uncertainty is revealed a recourse action may have to be made. Two possibilities are

studied: in the first one—facility outsourcing—extra capacity is paid for those facilities running out of

capacity; in the second one—customer outsourcing—an external service provider is paid for fulfilling

the missing capacity. The costs involved in this problem include the setup costs for the facilities, the

service costs, and the outsourcing costs. A neutral attitude towards risk is assumed for the decision

maker. The goal is to minimize the total setup cost for the facilities plus the expected service and

outsourcing costs. For this problem, a two-stage heuristic approach is developed. In the first stage,


Thursday Sessions


feasible solutions are generated using a GRASP procedure whose constructive step focuses on the

facility selection and the local search step focuses on customers’ assignment. In the second stage, Path

Relinking is applied to a pool of elite solutions. Throughout the procedure, approximations for the cost

function are used, since evaluating feasible solutions to overall problem is computationally expensive.

The results of a series of computational tests performed for evaluating the quality of the solutions

obtained are reported.

10:40 – 12:20

TA2 Semidefinite and Semi-infinite Programming Contributed Session

Chair: Tatiana Tchemisova Room: 6.2.49

1 - SOS versus SDSOS polynomial optimization

Mina Saee Bostanabad, PhD Student, University of Coimbra, CMUC Member, [email protected]

Co-author(s): João Eduardo da Silveira Gouveia, Assistant professor, University of Coimbra, CMUC Member,

[email protected]

Abstract

It is NP-hard to decide whether a polynomial is nonnegative, however, semidefinite programming can

be used to decide whether a polynomial is a sum of squares of polynomials (SOS) in a practically

efficient manner. In the context of polynomial optimization, it has become usual to substitute testing

for nonnegativity with testing for SOS. Since there are much fewer sums of squares than nonnegative

polynomials, we get only a relaxation and one that does not scale very well with the number of

variables and degree of the polynomial. Recently, Ahmadi and Majumdar introduced a more scalable

alternative to SOS optimization that they refer to as scaled diagonally dominant sums of squares

(SDSOS). The idea is searching for sums of squares of binomials, instead of general polynomials, which

leads to a more scalable SOCP problem.

In this presentation, we investigate the quantitative relationship between sums of squares of

polynomials and scaled diagonally dominant polynomials. More specifically, we use techniques

established by Blekherman to bound the ratio between the volume of the cones of these two classes of

polynomials, showing that there are significantly less SDSOS polynomials than SOS polynomials. This

drawback can be circumvented by using a recently introduced basis pursuit procedure of Ahmadi and

Hall that iteratively changes the polynomial basis to a more suitable relaxation. We illustrate this by

presenting a new application of this technique to the sensor network localization problem, where

previous SOS approaches suffered from poor scalability.

2 - Large scale moment/sum-of-squares hierarchy

Cédric Josz, Laboratory for Analysis and Architecture of Systems LAAS CNRS, [email protected]

Thursday Sessions


Co-author(s): Daniel Molzahn, Argonne National Laboratory, [email protected]

Abstract

The moment/sum-of-squares hierarchy, also known as Lasserre hierarchy, is a procedure to find global

solutions to polynomial optimization problems. While it has very strong theoretical guarantees, it is

limited in practice to small problems. In order to overcome this, we propose a multi-ordered hierarchy

where each constraint has its own relaxation order. It is much more tractable yet preserves the same

convergence guarantees. We will illustrate the approach step by step on some simple examples, and

interpret them through the lenses of measure theory. We also apply it to polynomial optimization

problems arising in electric power systems with several thousand variables and constraints.

3 - On optimal properties of special semi-infinite problems arising in

parametric optimization

Tatiana Tchemisova, University of Aveiro, [email protected]

Co-author(s): Olga Kostyukova, Maria Kurdina

Abstract

We consider a special Nonlinear Programming problem depending on integer parameters. For some

values of these parameters of this problem satisfies certain properties used in study of differential

properties of optimal solutions in parametric Semi-Infinite Programming. We deduce the conditions

guaranteeing the existence of the ``right'' parameters values, and propose an algorithm for their

determination. The conditions and the algorithm are essentially based on properties of a related linear-

quadratic Semi-Infinite problem.

10:40 – 12:20

TA3 Networks I Contributed Session

Chair: Maria Teresa Almeida Room: 6.2.48

1 - k-clubs with diameter constrained spanning trees

Filipa Duarte de Carvalho, Universidade de Lisboa, ISEG and CMAF-CIO-Centro de Matemática, Aplicações Fundamentais e Investigação Operacional, [email protected]

Abstract

Initially proposed in the context of social network analysis, the clique is the ideal model of a cohesive

subgroup. A clique possesses maximum familiarity and reachability among its members and retains its

structural properties in the event that one of its members leaves the group. However, in many

applications, it is unrealistic to require the presence of all possible direct links between the members of

a cohesive group. This has motivated the development of alternative graph-theoretic models of

cohesive groups in which different attributes of the clique are relaxed.

The k -club model relaxes the requirement of direct interaction between members of the group by

Thursday Sessions


allowing the existence of up to k 1k intermediary links between every pair of members. A k -club

of an undirected graph is defined as a subset of vertices inducing a subgraph of diameter at most k .

While the k -club model guarantees easy reachability for small values of k , connectivity may be lost

when a member leaves the group. In the event that the group remains connected, the number of

intermediaries between members may become extremely large, resulting in difficult reachability. To

keep the diameter of the residual graph within a small range in case of a vertex failure, a new path

constraint is added to the k -club model, yielding the fixed-length-path k -club (FLPkC), proposed in

this talk. The fixed-length-path k -club problem (FLPkCP) is to find a maximum cardinality FLPkC of an

undirected graph.

A characterization of the FLPkC model by the diameter of the spanning trees contained in the induced

subgraph is given. Based on the spanning tree characterization, integer linear programming

formulations and a decomposition approach are proposed for the FLPkCP, when 2k . Computational

results obtained on a set of randomly generated graphs are reported.

2 - A branch-and-cut algorithm and heuristics for the maximum weight

spanning star forest problem

Luidi Simonetti, Universidade Federal do Rio de Janeiro (UFRJ), [email protected]

Co-author(s): Rafael Melo, Universidade Federal da Bahia (UFBA), [email protected]

Abstract

Given an undirected simple graph ),( EVG in which each edge has an associated weight, the

Maximum Weight Spanning Star Forest Problem (MWSFP) consists in finding a spanning forest of G

composed of disjoint stars with the largest possible weight. The MWSFP is known to be NP-Hard. In an

unweighted graph, the problem consists in maximizing the number of edges in the forest. Note that in

this case the problem is complementary to the minimum dominant set.

The MWSFP appears in several practical applications in the commercial and industrial sectors, including

the activity of finding balanced allocations of customers to multiple distribution centers and the

configurations diversity problem in the automobile industry. In the field of computational biology, it

arises in the study of genomic sequences alignment and in researches regarding evolutionary trees.

We propose a new directed mixed integer programming formulation for the problem and show that it

provides better bounds than a standard undirected formulation available in the literature. We also

propose new valid inequalities and show how to strengthen others which are available in the literature.

We also propose two new heuristics to the MWSFP. The first one uses linear programming to select the

centers of the trees and afterwards solves the rest of the problem exactly. The second heuristic is

composed of constructive and local search algorithms.

Computational experiments are performed to evaluate the proposed methods. Preliminary results

show that our new formulation strengthened with the proposed valid inequalities can improve the

bounds obtained by the formulations available in the literature. Moreover, these results also show that

very good quality solutions can be obtained using the proposed heuristics.

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3 - Stronger extended formulation for the Steiner tree problem

Bartosz Filipecki, Université catholique de Louvain, [email protected]

Co-author(s): Mathieu Van Vyve, Université catholique de Louvain, [email protected]

Abstract

The Steiner tree problem (STP) is one of classical NP-hard combinatorial optimization problems. Given

an undirected graph with edge costs, the objective is to find a minimum-cost spanning tree of a subset

of vertices called terminals. Possible applications of the STP include network wiring and routing and

bioinformatics.

Current linear programming algorithms for the Steiner tree problem rely mostly on one of two

approaches - the bidirected cut relaxation (BCR) and hypergraphic formulations. These were proven to

have an upper bound on the integrality gap of 2 and 1.39 respectively, while the lower bounds based

on constructed graph instances are 1.16 and 1.14.

We propose a new hierarchy of improving extended formulations for the Steiner tree problem,

originating from BCR. We show that our hierarchy achieves a better lower bound on all graph instances

used to prove worst case lower bounds for both bidirected cut and hypergraphic formulations. Our

approach can be adjusted to solve variants of the STP, for example the Steiner forest problem, or

applied to hypergraphic formulation for further potential improvement.

4 - New models to identify large cohesive groups in networks

Maria Teresa Almeida, ISEG- Universidade de Lisboa; CMAF-CIO, [email protected]

Co-author(s): Raul Brás, ISEG-Universidade de Lisboa, CEMAPRE, [email protected]

Abstract

Graph and network models have been intensively used in the last decade to study complex systems in

many fields (e.g., social network analysis, telecommunications networks, bio-informatics, etc.). Since

large cohesive groups tend to be the most influent groups in the overall network, their identification

can be of great help to understand the behaviour of the whole system.

In this talk, we propose new graph models to identify large groups with high reachability and intense

inter-action among all their members. These models can be interpreted as clique relaxations with two

parameters, k and l , to enforce two distinctive features: (a) every pair of elements in the group is

separated by at most k hops; (b) every element belongs to at least l triplets linked pair-wise in the

group. First, we show that the problem of finding a maximum cardinality set of nodes with these two

features is NP-hard, for any integers k and l , and discuss the relationships between the new models

and q -cores, alpha-clusters and quasi-cliques. In the second part of the talk, we present integer

formulations for the maximization problem stated above designed with different sets of variables,

compare their linear programming relaxation, and their computational performance on randomly

generated and real-world networks.

10:40 – 12:20

Thursday Sessions


TA4 Routing I Contributed Session

Chair: Maria Cândida Mourão Room: 6.2.47

1 - Cooperative variable neighborhood search for the vehicle routing

problem with pickup and delivery

Olcay Polat, Pamukkale University, Department of Industrial Engineering, [email protected]

Abstract

This study considers the vehicle routing problem with pickup and delivery (VRPPD) which aims to

design a network of vehicles for dispatching a number of delivery goods from a depot to customers and

collecting a number of pickup goods from customers. In this problem, each customer may have a

demand of only pickup goods, only delivery goods or both. Since individual delivery or pickup demands

of customers cannot be split into smaller loads, delivery and pickup operations can be separately

executed by visiting customer twice (one for delivery and another for pickup) or simultaneously by

visiting customer once. All demands of each customer have to be satisfied by using capacitated and

identical vehicles. In this study, a mixed integer programing model of this problem class are presented

and a very efficient parallel approach based on variable neighborhood search (VNS) is proposed to

solve the problem. In this approach, an asynchronous cooperation with centralized information

exchange strategy is used for parallelization of the VNS approach, called cooperative VNS (CVNS). All

available problem sets of VRPDP have been solved with the CVNS and new best solutions have been

obtained for a number of benchmark instances.

2 - A variable neighborhood search based solution approach for designing

service network of beverage distribution

Leyla Ozgur Polat, Pamukkale University, Department of Industrial Engineering, [email protected]

Co-author(s): Olcay Polat, Pamukkale University, Dept. of Industrial Engineering, [email protected]; Can B. Kalayci,

Pamukkale University, Dept. of Industrial Engineering, [email protected]; Seckin Aydin, Pamukkale University,

Dept. of Industrial Engineering, [email protected]

Abstract

This study aims to design daily service network of beverage distribution by minimizing the total

distribution cost. Beverage distribution companies usually collect demand of local shops, markets and

restaurants on previous day and distribute beverages to demand points next day. This problem is

theoretically named as the heterogeneous fixed fleet vehicle routing problem (HFFVRP) which is a more

practical variant of vehicle routing problem (VRP). While the classical VRP assumes that the fleet owner

has unlimited number of vehicles from one type, it is assumed that the fleet owner has various types

and fixed number of vehicles in the HFFVRP variant. Similar to VRP, this problem variant allows vehicles

to make the delivery operations by visiting all clients at once with the aim of minimization of total

travel distance. In this study, an efficient hybrid approach is proposed to solve the problem. In this

approach, variable neighborhood search (VNS), savings heuristic and perturbation mechanism are

combined with the help of efficient neighborhood strategies. In order to show the effectiveness of the

approach, a well-known number of benchmark instances have been solved. Numerical results show

Thursday Sessions


that the developed approach achieved best-known solutions reported in the literature within

acceptable time limit. Then, a case study from the beverage industry has been addressed and solved.

The findings of this study indicate that the approach has a potential of enabling the decision maker to

make effective decisions related to the design of distribution networks.

3 - Performance comparison of modeling approaches for the steering of

international roaming problem

Maria da Conceição Fonseca, CMAF-CIO, Faculdade de Ciências, Universidade de Lisboa, [email protected]

Co-author(s): Carlos Martins, CEMAPRE, ISEG, Universidade de Lisboa, [email protected];

Margarida Pato, CMAF-CIO, ISEG, Universidade de Lisboa, [email protected]

Abstract

In the Steering of International Roaming Traffic (SIRT) problem, telecommunications operators offering

international roaming services need to decide to which foreign networks they should steer their

customers towards, in order to benefit from the best wholesale commercial conditions. This

operational managerial decision translates into a least-cost traffic routing problem faced by all mobile

telecoms providers offering the roaming service.

Under an optimization approach, five mixed integer linear programming models were developed,

corresponding to the most used international roaming agreements in the industry: Quantity and

Incremental agreements, agreement with Send-or-pay commitment clause (this can exist either on top

of a quantity or an incremental agreement), and Balanced-unbalanced agreement. A full year

managerial perspective is adopted, including interdependent periods and accounting for uncertainty in

the decisions.

The five models share a set of common features. Additionally, each one is characterized by a set of

individual features. These can relate to the conditions over parameters, the constraints or the objective

function of each model. Some models are conceptually similar while others differ more significantly.

Compact linearization techniques are used when the objective function is non-linear.

In the computational experiment carried out, the five models are studied both individually and

simultaneously in a Global model that reflects real-life situations faced by operators. The

computational experiment, performed with the standard CPLEX solver, confirms the soundness of the

models and the validity of their application to the SIRT problem on all instances tested. We consider

that the computational effort required is low, namely for the case closest to the reality faced by

telecom operators (Global model). Results are also evaluated according to some business sustainability

performance metrics.

4 - Arc routing involving dissimilarity issues

Maria Cândida Mourão, Universidade de Lisboa, Instituto Superior de Economia e Gestão, Lisboa,

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Portugal, CMAF-CIO, [email protected]

Co-author(s): Miguel Constantino, Universidade de Lisboa, Faculdade de Ciências, DEIO, Lisboa, Portugal, CMAF-

CIO, [email protected]; Leonor S. Pinto, Universidade de Lisboa, Instituto Superior de Economia e Gestão,

Lisboa, Portugal, CEMAPRE, [email protected]

Abstract

Arc routing methodologies are increasingly being fitted to solve some real-world problems. Such is the

case of a Portuguese company in charge of overseeing street car parking in the city of Lisbon. Routing

of vehicles collecting cash from parking meters, needs not only minimize total time but also include

dissimilarity to prevent possible robbery. Dissimilarity is defined along a time horizon, as routes in two

consecutive days should be as dissimilar as possible. Arc routing is suited to parking meter safes being

spread along the streets. We called this problem Dissimilar Arc Routing Problem (DARP).

DARP is defined on a mixed graph. As usual, edges represent narrow two way streets where zigzag

services are allowed. Arcs denote one way streets, or large two way streets needing service on both

directions. Nodes are street crossings, dead-end streets and a base point (the depot), where every

vehicle tour must start and end. Links representing streets with safes to be collected are named as

tasks. Services should be performed on a daily basis and the planning time horizon is five working days.

The problem aims to find a set of dissimilar tours, one per day, which minimizes total time.

To impose dissimilarity, each tour is divided into periods, and a threshold is set for the maximum

number of times a task is served during one same period. A methodology is proposed for the problem

and computational experiments are reported.

Acknowledgments: Authors want to thank Fundação para a Ciência e Tecnologia (FCT) as this work is

partially financed by FCT/MEC through national funds and when applicable co-financed by FEDER,

under the Partnership Agreement PT2020 and projects [UID/MULTI/00491/2013];

[UID/MAT/04561/2013]; [PTDC/MAT-NAN/2196/2014].

10:40 – 12:20

TA5 Non-Linear MIP Contributed Session

Chair: Pedro Castro Room: 6.2.46

1 - Mixed integer quadratic programming and an application in workload

assignment

Melis Mumcuoglu, Istanbul Technical University, [email protected]

Co-author(s): Elif Adakoy, Istanbul Technical University, [email protected]; Seray Sengul, Istanbul Technical

University, [email protected]; Gokhan Goksu, Istanbul Technical University, [email protected];

Kamil Orucoglu, Istanbul Technical University, [email protected]

Abstract

In this study, mixed-integer quadratic programming problem is considered. Three types of solution

methods are presented. First, Kelley's Cutting-Plane Method, which is a linear approximation to mixed-

integer linear programming problem, is explained. Latter, KKT conditions are applied to the quadratic

optimization problem in order to find the necessity and sufficient conditions of the proposed problem.




Thursday Sessions


Third, a sort-and-load algorithmic method is proposed. Finally, numerical examples in workload

assignment for each method are presented in order to compare the effectiveness and validity of the

proposed solutions.

2 - A time transformation approach in hybrid vehicles optimal design

Massimo De Mauri, K.U. Leuven, [email protected]

Co-author(s): Goele Pipeleers, K.U. Leuven, [email protected]; Jan Swevers, K.U. Leuven,

[email protected]

Abstract

In the latest years, powertrain hybridization has proved successful in enhancing passenger vehicles fuel

efficiency and further improvements are expected in future. However, to design hybrid vehicles

remains challenging: the wide variety of components allows for a great number of topologies, and,

once a topology is chosen, a set of design parameters like: battery capacity, engine/motor(s) size, gear

ratios, etc., must be accurately tuned.

In this presentation we focus on parameter tuning. The chosen methodology consists in solving an

optimal control problem in which the design parameters of the physical vehicle model are considered

as adjunctive optimization variables. The vehicle behavior is simulated for a series of common

maneuvers and its controls and parameters are optimized in order to minimize the fuel consumption

without depleting the battery on board. Such approach leads to general mixed integer non-linear

control problems. Although, many efforts in developing an efficient solution algorithm for this class of

problems have been made, this issue remains an open field of research. The present work consists in

the extension and adaptation of an existing reformulation based optimal control technique. The mixed-

integer problem is transformed into a continuous one via a time transformation.

First, the dynamics of the vehicle under design is modeled via a non-linear differential algebraic

equation involving discrete controls. Then, the continuous relaxation of the problem is solved using

multiple shooting on a predefined time discretization grid. If the resulting optimal controls do not

comply with the integrality requirements, the relaxation is solved again on a finer time grid. If the

maximum discretization resolution is hit before the process could find any integer-feasible solution, the

algorithm enters in a second phase. From the current solution an initial switching structure is extracted

and used to reformulate the problem as a multi-stage optimal control problem in which each stage

corresponds to a certain assignment of values for the discrete controls. The duration of each stage is

subject to optimization so that stages presenting a sub-optimal assignment for the discrete controls are

shrank to zero duration and the others are extended for as long as their relative assignment is optimal.

The result is an optimal and feasible solution for the original problem.

The results will be demonstrated using realistic model of a hybrid vehicle in which the sizes of engine,

motor and battery have to be optimized.


Thursday Sessions


3 - Reliable convex relaxation techniques for global optimization

Frederic Messine, LAPLACE-ENSEEIHT-INPT University of Toulouse, [email protected]

Co-author(s): Gilles Trombettoni, LIRMM, University of Montpellier, [email protected]

Abstract

In reliable (or rigorous) deterministic global optimization, all the computed bounds have to be certified

in the sense that no numerical error, due to floating point operations, can involve a wrong solution.

Interval arithmetic Branch and Bound algorithms which are developed since the 80th own this property

of reliability. However, some accelerating techniques have to be added in order to improve the

convergence of such reliable global optimization algorithms; for example, interval constraint

propagation, linear and affine relaxations. All these efficient added subroutines must also keep the

property of reliability. Some deterministic global optimization codes like BARON or COUENNE softwares

are really efficient but they are not reliable in a numerical point of view. Their intrinsic efficiency is

mainly based on convex relaxation techniques and on the use, during the iterations, of some local

optimization softwares such as IPOPT.

Indeed, the floating point local solutions returned at each step of COUENNE or BARON provide floating

point bounds which are not reliable because they approximate at the best the unique optima of the

convex relaxed programs.

In this work, we will present a way to correct these floating point lower bounds provided by the local

optimization solver in order to make them numerically reliable. Some properties will be provided

involving two different proofs: one based on linear programming (due to C. Jansson) and a second

original one based on the Lagrangian formulation. Some numerical tests applied to some global

optimization problems where all the functions are polynomial ones, will validate our approach by

showing that performing reliable bounds are not so expensive in term of CPU-time. An example

showing that BARON provides a wrong result induced by a numerical error will also be given and

discussed.

4 - Global optimization algorithm for MIQCPs featuring dynamic

piecewise relaxations

Pedro Castro, CMAF-CIO, University of Lisbon, [email protected]

Co-author(s): Pedro A. Castillo Castillo, McMaster University, [email protected];

Vladimir Mahalec, McMaster University, [email protected]

Abstract

Mixed-integer quadratically constrained problems (MIQCPs) with bilinear terms restricted to

continuous variables and linearly appearing binary variables, appear frequently in process systems

engineering. Well-known examples come from blending in petroleum refineries, and energy production

in hydroelectric power systems with a cascade of reservoirs. Binary variables are typically linked to

logistic constraints (connection between units) and to non-stationary operation, to cope, for example,

with hourly-changing electricity prices. Bilinear terms arise from mixing streams with unknown

compositions or from functions to compute power production. In this work, we focus on planning and

scheduling problems with an objective function linked to an economic performance indicator. MIQCPs

are non-convex problems that may be challenging to solve with gradient-based methods. Global

optimization of MIQCPs can prevent highly suboptimal solutions, being thus an inexpensive way of

Thursday Sessions


achieving savings in operation of thousands or even millions of dollars. State-of-the-art commercial

global optimization algorithms like BARON and GloMIQO mostly rely on spatial-branch-and-bound

(SBB) to iteratively reduce the variables domain and achieve -tolerance convergence, a process that can

be rather slow. Other methods that can be used to close the gap include piecewise relaxation and

optimality-based bound tightening (OBBT). Piecewise relaxation methods include piecewise McCormick

(PCM) and multiparametric disaggregation (MDT). Both require the specification of the number of

partitions per variable, which strongly affects the quality of the relaxation and the computational time

to solve the MILP. MDT has the advantage of scaling logarithmically rather than linearly in the number

of added binary variables. The disadvantage is that the coarsest setting is 10 partitions/per variable,

whereas PCM can start at 2. OBBT involves solving multiple optimization problems. It is typically

applied a limited number of times, using the weaker McCormick relaxation. In the proposed algorithm,

we use the PCM relaxation in OBBT and take advantage of parallelization to reduce the computational

wall time.

The main novelty of the proposed algorithm is that the number of partitions changes dynamically,

adjusting to the problem complexity. As the variables domain decreases, the relaxation problems

typically become easier to solve, meaning that more partitions can be specified to reduce the

optimality gap in the given time. The algorithm smartly distributes the computational time between

the piecewise relaxation and OBBT strategies. Computational results for a test of 10 example problems

from the literature reflect a better performance than BARON and GloMIQO. For one example, global

optimality was proven for the first time.

10:40 – 12:20

TA6 Sectorization and Parking Contributed Session

Chair: Joana Cavadas Room: 6.2.45

1 - Benders decomposition for the multi-period sales districting problem

Saranthorn Phusingha, School of Mathematics, The University of Edinburgh, United Kingdom, [email protected]

Co-author(s): Joerg Kalcsics, School of Mathematics, The University of Edinburgh, United Kingdom,

[email protected]

Abstract

In the sales districting problem, we are given a set of customers and a set of sales representatives in

some area. The customers are given as points distributed across the area and the sales representatives

have to provide a service at the customers' locations to satisfy their requirements. The task is to

allocate each customer to one sales representative. This partitions the set of customers into subsets,

called districts. Each district is expected to have approximately equal workload and travelling time for

each sales representative to promote fairness among them and the overall travelling distance should be

minimal for economic reasons. However, the real travelling distance is often hard to calculate due to

many complicating factors i.e. time windows or unexpected situations like traffic jams resulting in a loss

of service. Therefore, one of the alternative ways is to approximate the travelling distance by

considering geographical compactness instead.

Thursday Sessions


We now extend this problem to be more realistic by considering that each customer requires recurring

services with different visiting frequencies like every week or two weeks during the planning horizon.

This problem is called the 'Multi-Period Sales Districting Problem'. In addition to determining the sales

districts, we also want to get the weekly visiting schedule for the sales representative such that the

weekly travelling distances are minimal and the workload and travelling time are balanced each week.

Although the problem is very practical, it has been studied just recently. In this presentation, we focus

on the scheduling problem for one sales representative in a specific district, which is already an NP-

hard problem. We start by proposing a mixed integer linear programming formulation. Afterwards, we

develop and implement a Benders Decomposition to solve the problem, exploiting the structure of the

formulation. We also consider modifications of the method to enhance the performance of the

algorithm.

2 - Sectorization problems with multiple criteria

Luís Miguel Bandeira, Faculdade de Engenharia da Universidade do Porto / INESC TEC, [email protected]

Co-author(s): Ana Maria Rodrigues, INESC TEC / CEOS.PP – Centro de Estudos Organizacionais e Sociais do P. Porto

do Instituto Politécnico do Porto, [email protected]; José Soeiro Ferreira, Faculdade de Engenharia da Universidade

do Porto / INESC TEC, [email protected]

Abstract

Sectorization consists in the division of a given territory into regions or districts, generally, to achieve

some goal or to facilitate an activity according to some constraints. Sectorization problems appear in a

large variety of contexts, such as political districting, definition of sales and delivery regions and the

design of emergency and security areas.

Different criteria are usually taken into account when dealing with such problems. Equilibrium (sectors

must be balanced), compactness (round or square shapes are better than “U” or “octopus” shapes) and

contiguity (each sector must be composed by one body) are current requirements when designing

sectors. However, depending on the application, other criteria may be introduced (or adapted) to

better reflect the purpose of the specific application.

The presentation will start by introducing a recent general method to solve Sectorization problems,

which is inspired in Electrostatics, and by explaining how it can handle Multiple Criteria. Afterwards,

applications will be considered, an example being a Waste Collection real case. Application are tackled

in two interrelated phases: the first phase is a sectorization phase, in order to simplify the problem and

to contemplate various criteria defined by the authors; the second phase resorts to metaheuristics,

adapted to the specificities of the application under study.

Finally, the presentation will include the obtained computational results and will provide some

conclusions.


Thursday Sessions


3 - Effect of the learning factors on the dynamic assignment problem of

parking slots

Mustapha Ratli, LAMIH/ UVHC, [email protected]

Co-author(s): Abdessamad Ait El Cadi, LAMIH/ UVHC, [email protected]; Bassem Jarboui,

M.O.D.I.L.S / Université de Sfax, bassem [email protected]; Thierry Delot, LAMIH/ UVHC, thierry.delot@uni-

valenciennes.fr

Abstract

In urban logistics, the parking management is one of the most important issues for modern cities. It

helps managing the traffic and reduce its impact on the environment. The present work deals with the

dynamic assignment problem of the parking slots. The aims are to provide a global satisfaction of all

customers and maximize the parking lots occupancy. A dynamic assignment problem consists of solving

a sequence of assignment problems over time. At each time period decisions, must be made as to

which resources and tasks will or will not be assigned to each other. Assignments which are made at

earlier time periods affect which assignments can be made during later time periods, and information

about the future is often uncertain. In this paper, we propose a MIP formulation with a time partition,

throw a set of decision points, to handle the dynamic aspect. To solve this problem, we propose a

hybrid approach using Munkres’ Assignment Algorithm, a Local search and an Estimation of

Distribution Algorithm (EDA) with a reinforcement learning. We tested our approach with and without

the learning effect. Our approach is efficient; we were able to manage a set of 10 parking lots over 120

days (problems with up to 7000 parking slots and 13000 requests per day). The saving is up to 80% and

the results show, also, the benefit of the learning effect.

4 - Game-theoretic approach to transit and parking planning under

competition

Joana Cavadas, University of Coimbra, [email protected]

Co-author(s): Vikrant Vaze, Thayer School of Engineering Dartmouth College, USA, [email protected];

António Pais Antunes, University of Coimbra, [email protected]

Abstract

The research presented in this paper aims to model and analyze the capacity and pricing decisions

made by a transit operator and a parking company that compete for users in an urban setting. To this

end, a two-stage game-theoretic approach is developed. During the game’s first stage, decisions such

as parking capacity, transit frequencies and fleet size are made. Pricing schemes are determined in the

second stage of the game, assuming first-stage decisions to be known and fixed. Cities are divided into

zones and travelers are assumed to choose between driving, transit, and no-travel alternative. Modal

choices are described by logit models of the generalized travel costs of all modes. The subgame-perfect

pure strategy Nash equilibrium is used as the solution concept for solving this game. Due to the

extremely large size of the players’ overall decision spaces, the solution approach consists of: 1)

determining a pure strategy Nash equilibrium for the second-stage game, 2) approximating the second-

stage game’s equilibrium payoffs as functions of first-stage decisions, and 3) using these second-stage

payoff approximations to find the first-stage game’s pure strategy Nash equilibrium. Our approach


Thursday Sessions


provides a good understanding of how capacity and pricing decisions influence operator finances and

users’ mode choice behavior, while explicitly accounting for the substitution effects between transit

and car.

13:50 – 15:05

TB1 Copositive Optimization I

Organized Session

Organizer/Chair: Paula Amaral Room: 6.2.50

1 - Copositive approach to adjustable robust optimization

Markus Gabl, Department of Statistics and Operations Research, University of Vienna, [email protected]

Co-author(s): Immanuel Bomze, Department of Statistics and Operations Research, University of Vienna,

[email protected]

Abstract

Adjustable robust optimization aims at solving problems under uncertainty in a first stage; the second

stage decisions can be adjusted after uncertainty is removed. Hence, the objective is to identify the

best solution among those which in any case allow for feasible adjustment of the second stage

variables. Obviously there is greater flexibility than in a general uncertainty setting and thus less

conservative strategies are viable. However, the computational cost rises, also for problems where the

constraint-coefficients of the second stage variables are affected by uncertainty as well (uncertain

recourse). This talk reports on research efforts (in progress) to approach these issues by applying

copositive optimization techniques.

2 - Quadratic optimization with uncertainty in the objective function

Michael Kahr, University of Vienna, [email protected]

Co-author(s): Markus Leitner, VGSCO, VCOR & ISOR, University of Vienna, [email protected];

Immanuel Bomze, VGSCO, VCOR & ISOR, University of Vienna, [email protected]

Abstract

During the last decades the importance of considering data uncertainty in optimization problems has

become increasingly apparent, since small fluctuations of input data may lead to comparably bad

decisions in many practical problems when uncertainty is ignored. If the probability distribution of the

uncertain data is unknown (or cannot be estimated to sufficient precision), a common technique is to

estimate bounds on the uncertain data (i.e., define uncertainty sets) and to identify optimal solutions

that are robust against data fluctuations within these bounds. This approach leads to the robust

optimization paradigm that allows to consider uncertain objectives and constraints.

Optimization problems where only the objective is uncertain arise, for instance, prominently in the

Thursday Sessions


analysis of social networks. This stems from the fact that the strength of social ties (i.e., the amount of

influence individuals exert on each other) or the willingness of individuals to adopt and share

information can, for example, only be roughly estimated based on observations. A fundamental

problem arising in social network analysis regards the identification of communities (e.g., work groups,

interest groups), which can be modeled naturally with the framework of quadratic optimization.

We investigate data uncertainty in the objective function of (standard) quadratic optimization problems

(StQP) while considering different uncertainty sets, and derive implications for the complexity of robust

variants of the corresponding deterministic counterparts. Our preliminary results indicate that

considering data uncertainty in an StQP results in another StQP of the same complexity if ellipsoidal,

spherical or boxed uncertainty sets are assumed. Moreover we discuss implications when considering

polyhedral uncertainty sets.

3 - An exact copositive representation for the discrete ordered median

problem

Justo Puerto, Department of Statistics and Operations Research, Faculty of Mathematics, University of Seville, [email protected]

Abstract

The discrete ordered median problem (DOMP) represents a generalization of several well-known

discrete location problem, such as p-median, trimmed mean, etc. The problem was introduced by

Nickel in 2001 and later studied by Boland et al. in 2006, among many other papers. DOMP is an NP-

hard problem as an extension of the p-median problem.

Nickel 2001 developed a quadratic integer programming formulation of the DOMP. However, no

solution method was proposed in that paper for this quadratic formulation and thus, there is no

attempt to determine how effective integer programming approach can be in solving the DOMP. On the

contrary, since then several linearizations have been proposed to solve DOMP. One of the drawbacks of

this latter approach is the big gap between the optimal solution and the linear relaxations of the MIP

formulations (in average around 30%).

Nowadays, semidefinite programs have proven to be important tools for developing approximation

algorithms for NP-hard optimization problems. Motivated by the numerical and theoretical success of

SDP for the max-cut problem, QAP, etcetera; we study the efficacy of using SDP to provide new SDP

relaxations for the DOMP. We will give a new quadratically constrained quadratic formulation for DOMP

and show that it admits an exact reformulation as a linear problem over the cone of completely

positive matrices.

Thursday Sessions


13:50 – 15:05

TB2 Graphs and Optimization Organized Session

Organizer/Chair: Domingos M. Cardoso Room: 6.2.49

1 - The train frequency compatibility problem

Jorge Orestes Cerdeira, Departamento de Matemática and Centro de Matemática e Aplicações (CMA),

Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, [email protected]

Co-author(s): Ricardo L. Saldanha, SISCOG Sistemas Cognitivos, S.A., [email protected]; Pedro Cristiano Silva,

Centro de Estudos Florestais (CEF), Instituto Superior de Agronomia, Universidade de Lisboa, [email protected]

Abstract

Line planning is the general problem of designing line plans for a public transportation system that

meet given passenger demands, defined in terms of passenger volumes to be transported between

pairs of stations in the transportation network. A line plan is given in terms of a set of train lines each

with its own train frequency associated. A train line defines the stations of the arrival and departure

events shared by a set of trips that occur periodically with a given train frequency (e.g. a train every 15

minutes). After the line planning problem is solved, times can be assigned to the departure and arrival

events of the periodic trips to obtain an operational timetable. This is called the timetable generation

problem.

In order to make sure that the line planning produces a solution based on which it is possible generate

a timetable that satisfies the traffic safety rules, it is crucial to introduce a feasibility check in the line

planning solving process. This feasibility check tests, for each track section of the network, if the train

frequencies of the lines using that section are compatible with the traffic rules. Traffic rules (in double

track lines) state that trains must be temporally separated by at least a given safety headway. This

feasibility check is what we call the train frequency compatibility (TFC) problem. The problem involves

assigning times to trips in order to maximize the minimum time separation between pairs of trips, and

checking if this maximum minimum time separation is greater or equal than the safety headway.

TFC can be mathematically described as follows. Given a collection A of (possible repeated) positive

integers (the train frequencies), 0i (the starting time of one of the periodic trips i ), for every

Ai such that )(min ji jnimz (the minimum time separation between trips i and

j ), for Aji and nm, is maximum.

In this talk we give a MILP formulation for TFC, describe a procedure to obtain bounds on maximum z ,

and report some computational results.

Acknowledgements: JOC and PCS were partially supported by the Fundação para a Ciência e a

Tecnologia (Portuguese Foundation for Science and Technology) through strategic projects

UID/MAT/00297/2013 (CMA), and UID/AGR/002389/2013 (CEF).

Thursday Sessions


2 - A semidefinite programming approach to the 2-club problem

Carlos J. Luz, CIDMA / University of Aveiro, Portugal, [email protected]

Abstract

A 2-club of a graph is a subset of vertices inducing a subgraph of diameter at most two. The 2-club

problem consists of finding a maximum cardinality 2-club in a given undirected graph. In this talk, two

semidefinite programming relaxations for the 2-club problem as well as some of their properties are

presented. Also, two heuristics for extracting 2-clubs from the above mentioned relaxations are

described.

Related computational results are succinctly reported.

Acknowledgements: This work was partially supported by Portuguese Foundation for Science and

Technology - FCT, through the CIDMA - Center for Research and Development in Mathematics and

Applications, within project UID/MAT/ 04106/2013.

3 - Lexicographic polynomials of graphs

Domingos M. Cardoso, Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal, [email protected]

Co-author(s): Paula Carvalho, CIDMA, Department of Mathematics, University of Aveiro, [email protected];

Paula Rama, CIDMA, Department of Mathematics, University of Aveiro, [email protected]; Slobodan K. Simić,

Mathematical Institute SANU, Belgrade, Serbia, [email protected]; Zoran Stanic, Faculty of

Mathematics, University of Belgrade, Serbia, [email protected]

Abstract

For a (simple) graph H and non-negative integers dccc ,...,, 10 0dc , kd

kk HcHp

0.)( is the

lexicographic polynomial in H of degree d , where the sum of two graphs is their join and kk Hc . is the

join of kc copies of kH . The k th power of H with respect to the lexicographic product is denoted kH

)( 10 KH . The spectrum (if H is regular) and the Laplacian spectrum (in general case) of )(Hp are

determined in terms of the spectrum of H and 'kc s and several combinatorial properties are

presented. Constructions of infinite families of cospectral or integral graphs are also presented.

Acknowledgements: This work was supported by Portuguese Foundation for Science and Technology -

FCT, through the CIDMA - Center for Research and Development in Mathematics and Applications,

within project UID/MAT/ 04106/2013.


Thursday Sessions


13:50 – 15:05

TB3 Variational Inequalities and PDE-Constrained Optimization II Organized Session

Organizer/Chair: Livia Susu Room: 6.2.48

1 - Ill-posed backward nonlinear hyperbolic evolution Maxwell’s equations

Dehan Chen, University of Duisburg-Essen, Germany, [email protected]

Abstract

This talk is concerned with Tikhonov regularization and optimization for ill-posed backward nonlinear

hyperbolic evolution Maxwell equations. Through the use of an appropriate Tikhonov regularization,

we recover the exact initial data from the final observation data with L2-noisy. The well-posedness and

convergence behavior of the regularized solutions are established. In particular, we verify the

variational source condition for the inverse problem and derive Hölder type convergence rates under

an appropriate parameter choice and Sobolev-type priori assumptions on initial values. It is worth

mentioning that our results can be applied to the Bean’s critical state model in type-II

superconductivity. The major tools used here include regularization theory and mathematical theory

for Maxwell’s equations.

2 - Total variation regularization of multi-material topology optimization

Florian Kruse, University of Graz, Austria, [email protected]

Co-author(s): Christian Clason, University of Duisburg-Essen, Germany, [email protected];

Karl Kunisch, University of Graz, Austria, [email protected]

Abstract

In this talk we are concerned with the following problem: Given measured data in a domain, determine

the distribution u of finitely many materials in this domain, i.e., muuuu ,...,, 10 a.e., such that the

data induced by u matches the given data to a certain extent.

To model this we use a multi-material topology optimization problem subject to an elliptic PDE in which

the control u enters as diffusion coefficient.

This problem is ill-posed and does not have a solution, in general. However, after suitable regularization

we obtain a problem that has optimal solutions and, although still nonsmooth and nonconvex, can be

solved efficiently.

We present theoretical and numerical results for this new approach to inverse problems and topology

optimization.

Thursday Sessions


3 - Inverse point source location with the Helmholtz equation

Philip Trautmann, University of Graz, Austria, [email protected]

Co-author(s): Konstantin Pieper, FSU, Tallahassee, USA, [email protected]; Tang Quoc Bao, University of Graz,

Austria, [email protected]; Daniel Walter, TU Munich, Garching, Germany, [email protected]

Abstract

In this talk the reconstruction of a linear combination of acoustic monopoles from given noisy

measurements of the acoustic pressure at M observation points is addressed. For the solution of this

problem a family of regularized optimal control problems involving the Helmholtz equation is used.

These optimization problems are posed in the space of measures and the regularization functional is

given by a weighted version of the total variation norm for measures which is non-smooth and favors

solutions with a sparse support. To prove well-posedness of these optimization problems in a general

setting the weights in the regularization functional are chosen unbounded in the observation points.

Moreover, optimality conditions and conditions for the recovery of the exact sources in the case of

small noise are derived. The regularized problems are solved by an accelerated conditional gradient

method. The Helmholtz equation is discretized by linear finite elements. Finally, numerical experiments

are presented which suggest that an appropriate choice of the weighting function increases the quality

of the reconstructions over the unweighted approach.

13:05 – 15:05

TB4 Derivative Free Optimization

Organized Session

Organizer/Chair: Margherita Porcelli Room: 6.2.47

1 - Rethinking the benchmarking of derivative free optimizers

Anne Auger, Inria and École Polytechnique, France, [email protected]

Co-author(s): Dimo Brockhoff, Inria and École Polytechnique, [email protected]; Nikolaus Hansen, Inria and

École Polytechnique

Abstract

Benchmarking is a compulsory task to measure quantitatively performance of algorithms. It helps in

understanding strength and weaknesses of algorithms and puts at a standardized test various methods

that would not be comparable otherwise. Ultimately, benchmarking studies should have a predictive

power in regard to "real word". Yet, benchmarking is not a trivial task. In the past, bias on the choice of

test functions or certain use of performance displays led to misrepresentation of results.

In this talk, we will discuss typical shortcomings from past benchmarking studies and review recent

efforts that have been made towards better and easier benchmarking. Particularly, we will present the

Comparing Continuous Optimizers (COCO) platform (https://github.com/numbbo/coco) that aims at

facilitating the benchmarking of derivative free (continuous) optimizers and implements a thorough

methodology for benchmarking algorithms.




https://github.com/numbbo/coco

Thursday Sessions


We will present the underlying methodology behind COCO, emphasize what is typically different to

previous benchmarking studies and give a small guided tour of COCO.

An overview of the latest extensions of COCO towards bi-objective and constrained benchmarking will

be given.

2 - MultiGLODS: global and local multiobjective optimization using direct

search

Ana Luísa Custódio, Universidade Nova de Lisboa, [email protected]

Co-author(s): José Aguilar Madeira, Universidade de Lisboa and ADM-ISEL, [email protected]

Abstract

The optimization of multimodal functions is a challenging task, in particular when derivatives are not

available for use. Recently, in a directional direct search framework, a clever multistart strategy was

proposed for global derivative-free optimization of single objective functions. The goal of the current

work is to generalize this approach to the computation of global Pareto fronts for multiobjective

multimodal derivative-free optimization problems. The proposed algorithm alternates between

initializing new searches, using a multistart strategy, and exploring promising subregions, resorting to

directional direct search. Components of the objective function are not aggregated and new points are

accepted using the concept of Pareto dominance. The initialized searches are not all conducted until

the end, merging when start to be close to each other. We will describe the algorithmic structure

considered, present the main associated theoretical results, and report related numerical experience

that evidences the quality of the final solutions generated by the new algorithm and its capability in

identifying approximations to global and local Pareto fronts of a given problem.

3 - Optimizing structured problems without derivatives and other new

developments in the BFO package

Margherita Porcelli, University of Florence, Italy, [email protected]

Co-author(s): Philippe L. Toint, University of Namur, Belgium, [email protected]

Abstract

The talk will introduce techniques that allow the solution of large structured optimization problems in

the context of random pattern search in nonlinear optimization. They result from a re-interpretation of

techniques proposed by Price and Toint, but introduce some significant new ideas which prove to be

very efficient. Also, polynomial interpolation models will be adapted to the partial separable case and

employed through the exploitation of the search step feature. Examples will be shown where partially

separable problems in more than 10000 variables are solved by the BFO package with a very small

number of (complete) function evaluations. If time allows, a short review of other new features of the

derivative-free optimizer BFO will be presented, covering the support of categorical variables, new

optimizer's training strategies and options-file features.

Thursday Sessions


13:50 – 15:05

TB5 Clustering Contributed Session

Chair: Graça Gonçalves Room: 6.2.46

1 - q-vars: a new heuristic to select the relevant features for clustering

Stefano Benati, School of international studies, Università di Trento, Italy, [email protected]

Co-author(s): Sergio Garcia, School of Mathematics, University of Edinburgh, United Kingdom, Sergio.Garcia-

[email protected]; Justo Puerto, IMUS, Universidad de Sevilla, Spain, [email protected]

Abstract

We considered the problem of selecting relevant features in clustering problems, out of a data set in

which many features are useless, or masking. We must discard the masking variables before running

the clustering algorithm, otherwise the results are biased by useless information. The problem is

characterized by the following input data: The set of units (to cluster), the set of features (to separate

between relevant and useless), the set of (tentative, preliminary) cluster centers. Then distances, or

dissimilarity, between every unit and center are calculated for every feature. We formulated the

feature selection problem as finding the subset of features such that the total sum of the distances

from the units to their closest center is minimized. This is a new combinatorial problem that we have

shown to be NP-complete. We proposed some exact and heuristic solution methods. We carried out an

extensive computational comparison between them and we determined that a heuristic, that we called

q-vars calculates the optimal solution quickly. The steps of q-vars are deceptively simple. The method

begins by selecting a random set of features, then, in the first step, it calculates the optimal assignment

of units to centers using only the selected features. In the second step, it calculates the optimal

features corresponding to the assignments calculated before. Next, the algorithm iterates between

steps 1 and 2 till convergence.

In our second group of simulations, the q-vars is combined with the k-means to test the ability of the

methodology to recover the true cluster structure of some simulated data, and we found that the

method is also better than the existing methodologies suggested in the statistic literature.

2 - New results in clustering data that are connected through a network

Antonio Manuel Rodríguez-Chía, University of Cádiz, Spain, [email protected]

Co-author(s): Stefano Benati, University of Trento, [email protected]; Justo Puerto, University of Sevilla,

[email protected]

Abstract

A new combinatorial model for clustering is proposed for all applications in which individual and

relational data are available. Individual data refer to the intrinsic features of units, they are stored in a

matrix D , and are the typical input of all clustering algorithms proposed so far. Relational data refer

to the observed links between units, representing social ties such as friendship, joint participation to

social events, and so on. Relational data are stored in the graph ),( EVG , and the data available for

clustering are the triple ),,( DEVG , called attributed graph. Known clustering algorithms can take



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advantage of the relational structure of G to redefine and refine the units membership. For example,

uncertain membership of units to groups can be resolved using the sociological principle that ties are

more likely to form between similar units. The model proposed here shows how to take into account

the graph information, combining the clique partitioning objective function (a known clustering

methodology) with connectivity as the structural constraint of the resulting clusters. The model can be

formulated and solved using Integer Linear Programming and a new family of cutting planes. Moderate

size problems are solved, and heuristic procedures are developed for instances in which the optimal

solution can only be approximated. Finally, tests conducted on simulated data show that the clusters

quality is greatly improved through this methodology.

3 - Comparative study of mathematical formulations for the K clusters

with fixed cardinality problem

Graça Gonçalves, FCT-UNL, CMA-UNL, [email protected]

Co-author(s): Lídia Lourenço, FCT-UNL, CMA-UNL, [email protected]

Abstract

We present the k clusters with fixed cardinality problem and we propose mixed-integer linear

programming formulations for the same problem. All the mixed integer linear models are compared

from a theoretical and practical point of view. The continuous linear relaxation bounds of the

developed models are tested on randomly generated instances, by using standard software, with

promising results.

13:05 – 15:05

TB6 Facility Location Contributed Session

Chair: Isabel Correia Room: 6.2.45

1 - A continuous formulation for the multi-row facility layout problem

with rectilinear distances

Manuel Vieira, FCT NOVA, [email protected]

Co-author(s): Miguel Anjos, Polytechnique Montréal, [email protected]

Abstract

The multi-row facility layout problem (MRFLP) is the most general version of row layout problems. An

instance of the MRFLP has a given number of rows to which the machines can be assigned, the

machines all have the same height (equal to the row height), the distances between adjacent rows are

equal, and machines can in general be assigned to any row.

The objective is to minimize the sum of the pairwise weighted distances, where the distances are

measured using the rectilinear (or Manhattan) distance.

We present a mixed integer linear programming formulation which is continuous in both dimensions x

Thursday Sessions


and y , where x represents the position within rows and y is the row assigned to each machine.

Despite y being a continuous variable, optimal solutions are attained with y integer.

We present our computational results and compare them with other published formulations.

2 - Ranking-based random search algorithm for discrete competitive

facility location

Algirdas Lancinskas, [email protected]

Co-author(s): Blas Pelegrin, University of Murcia, [email protected]; Pascual Fernandez, University of Murcia,

[email protected]; Julius Zilinskas, Vilnius University, [email protected]

Abstract

The competitive facility location problems are important for firms providing a service or goods to

customers and have to compete with other firms for the market in a certain geographical area. The

determination of locations for the new facilities usually leads to solution of global optimization

problem with various properties and constraints.

Our research is focused on solution of a discrete competitive facility location problems for an entering

firm which is aimed at selection of optimal locations for a set of new facilities subject to maximization

their market share. The proposed heuristic algorithm for selection of the optimal locations is based on

random search with ranking of candidate locations.

The performance of the proposed algorithm has been experimentally investigated by solving a set of

competitive facility location problem instances of different scope and models for customer behavior

using real data of near 7000 demand points in a certain geographical area.

The results of the investigation shows that the proposed algorithm is suitable to solve discrete CFLP for

firm expansion of different scope and is competitive with the state-of- the-art algorithms present in the

literature.

3 - A dynamic capacitated location problem with modular capacity

adjustments and flexible demand satisfaction

Isabel Correia, Centro de Matemática e Aplicações / Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, [email protected]

Co-author(s): Teresa Melo, Business School, Saarland University of Applied Sciences, Germany,

[email protected]

Abstract

We consider a facility location problem that takes into account changing trends in customer demands

and costs. To this end, new facilities can be established at pre-specified potential locations and initially

existing facilities can be closed over a planning horizon. Furthermore, all facilities operate with modular

capacities that can be adjusted through expansion or contraction over multiple time periods. Our

problem addresses situations in which space and equipment can be rented or operations can be

subcontracted. This allows a company to dynamically adjust the configuration of its facilities, e.g. to

respond to seasonal demand changes. A further distinctive feature of our problem is that two customer


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segments are considered with different sensitivities to delivery lead times. Customers in the first

segment require timely demand satisfaction, whereas customers in the second segment tolerate late

deliveries. A tardiness penalty cost is incurred to each unit of demand that is satisfied with delay. We

propose two alternative mixed-integer linear programming formulations to redesign the facility

network over the planning horizon at minimum cost. Since one of the formulations has a significantly

smaller number of binary variables, one may conjecture that it would be favorable to use this

formulation to solve large problem instances using a general-purpose solver. The validity of this

conjecture is investigated through a computational study. Furthermore, both models are enhanced

with several types of inequalities that improve the lower bounds provided by their linear relaxations.

Test instances were randomly generated following three different demand scenarios. In scenario 1,

customer demand is irregular. Scenarios 2 and 3 are associated with trapezoidal shapes. Scenario 2

represents a typical product life cycle with a growth stage followed by a maturity phase and ending

with gradual decline. In scenario 3, customer demand rates follow an inverted trapezoid, the latter

representing an economic downturn followed by market recovery. In this case, demand variations go

through three phases (i.e. contraction, recession and growth). The numerical results indicate that the

performance of the formulations is impacted by the shape of the demand distribution and the

maximum allowed delivery delay. In particular, no dominance relationship can be established between

the two formulations. The model enhancements have shown to be very useful to identify optimal

solutions and to provide tight lower bounds. Useful insights are also derived from analyzing the trade-

offs derived from location and capacity scalability decisions, and the impact of permitting delays in

demand fulfillment.

16:45 – 18:00

TC1 Copositive Optimization II

Organized Session

Organizer/Chair: Paula Amaral Room: 6.2.50

1 - Factorizations for completely positive matrices based on alternating

projections

Patrick Groetzner, University of Trier, [email protected]

Co-author(s): Mirjam Dür, University of Trier, [email protected]

Abstract

Many combinatorial and nonlinear problems can be reformulated as convex problems using the

copositive and the completely positive cone. Therefore it is of interest whether a matrix is an element

of one of these cones. A certificate for a matrix to be completely positive is its non-negative

factorization. In this talk I will present a method to derive these factorizations using alternating

projection between certain non-convex sets.

Alternating projection is a common method to find points in the intersection of two or more convex

sets. This has recently been extended to alternating projection on manifolds and non-convex sets, as

used in our approach.

The presented method delivers factorizations for almost all matrix in a few seconds.

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2 - On regular simplicial division in branch-and-bound algorithms for

copositivity detection

Leocadio G. Casado, University of Almeria, [email protected]

Co-author(s): José M.G. Salmerón, University of Almeria, [email protected]; Paula Amaral, University Nova de

Lisboa, [email protected]; Julius Zilinskas, Vilnius University, [email protected]; Eligius M.T. Hendrix, University

of Málaga, [email protected]

Abstract

The problem of determining if a matrix is co-positive is very useful in combinatorial and quadratic

optimization, among others, with several fields of application. Here we study a branch-and-bound

(BnB) algorithm with the unit simplex as the search region to solve this problem.

In order to avoid the evaluation of an infinite number of points, several studies use a co-positivity test.

This test is based on calculating a lower bound for every sub-simplex edge using its vertices in the

formulation. The test checks whether the lower bound of a simplex is positive.

The possible results of the BnB algorithm are:

i) a point invalidating co-positiveness is found,

ii) all sub-simplices, as leaves of the BnB tree, have been checked on co-positveness and

iii) the algorithm ends without a certificate of co-positiveness.

The latter is needed to guarantee the algorithm finishes in a finite number of steps. For iii), the

termination criterion do not further process a sub-simplex when its lower bound is greater than a

threshold epsilon. To build the BnB search tree, a sub-simplex is partitioned based on evidences about

co-positiveness on its edges. Additionally, due to i), ii) and memory requirements, the next sub-simplex

to process among the leaves of the search tree node plays an important role in the efficiency of the

algorithm. We study which sub-simplex should be selected next and a division generating regular sub-

simplices. A regular simplex requires less memory, because only its centre and radius is stored.

Additionally, its round shape seems to be appropriate to get better bounds than in a needle shape one,

which helps to iii).

For dimensions greater than four, simplicial regular division is not possible without overlap. This

introduces new challenges a) to avoid same area evaluation by using a covering test, which is

computationally simple, but it could be performed many times during the algorithm and b) to

determine a better regular division than the uniform passive one in order to reduce the overlap, the

generated sub-tree and the number of vertex evaluations.

3 - Completely positive formulations for minimax fractional quadratic

problems

Paula Amaral, DM and CMA, FCT, University Nova de Lisboa, [email protected]

Co-author(s): Immanuel Bomze, Department of Statistics and Operations Research, University of Vienna,

[email protected]

Abstract

In this presentation we address min-max problems of fractional quadratic functions over a polytope.

The fractional min-max problem occurs, among others in the study of worst-case analysis when

different scenarios are under evaluation. Fractional programs are in general non-convex programs and

exact methods require the existence of good lower bounds. The merits of copositive and completely

Thursday Sessions


positive optimization are recognized in the reformulations of hard optimization problems, such as

continuous non convex, mixed integer quadratic, continuous and mixed integer fractional quadratic

problems. An important feature of completely positive formulations is that doubly positive

(semidefinite and nonnegative) relaxations give tight lower bounds. In this talk we present completely

positive formulations for min-max fractional quadratic problems and study the quality of the lower

bounds obtained using the relaxation of the completely positive cone. Computational experience

compare the lower bound obtained with the one provided by Baron, and show that for instances that

Baron could not solve, feeding Baron with these lower bounds has a significant impact in achieving

optimality.

16:45 – 18:00

TC2 Stochastic and Randomized Algorithms

Organized Session

Organizer/Chair: Clément Royer Room: 6.2.49

1 - Stochastic variance reduced methods based on sketching and

projecting

Robert M. Gower, INRIA, École Normale Supérieure, Paris, France, [email protected]

Co-author(s): Peter Richtarik, School of Mathematics, University of Edinburgh, United Kingdom,

[email protected]; Francis Bach, INRIA, École Normale Supérieure, Paris, France, [email protected]

Abstract

We present a new perspective on stochastic variance reduced (SVR) methods as methods that maintain

an estimate of the Jacobian of an auxiliary vector valued function. This auxiliary vector valued function

is formed by stacking the individual data functions from the empirical risk minimization problem.

Through this observation we extend the class of SVR methods by updating the Jacobian estimate using

randomized sparse sketches of the true Jacobian. By choosing different randomized sketches we

recover know methods: the SAG and SAGA method, their mini-batch variants and even non-uniform

sampling variants.

These new SVR methods all converge linearly, as dictated by a single convergence theorem. When

specialized to known methods, our convergence theorem recovers the best known convergence results

for SAGA, and furthermore, we obtain new results for mini-batch and non-uniform sampling variants of

SAGA. Thus our work unites all SAGA variants under one framework.

2 - Upper-confidence Frank-Wolfe algorithms for convex bandit

optimization: fast rates

Vianney Perchet, CMLA, ENS Paris Saclay, France, [email protected]

Co-author(s): Quentin Berthet, University of Cambridge, United Kingdom, [email protected]

Abstract

We consider the problem of bandit optimization, inspired by stochastic optimization and online

learning problems with bandit feedback. In this problem, the objective is to minimize a global loss


Thursday Sessions


function of all the actions, not necessarily a cumulative loss. This framework allows us to study a very

general class of problems, with applications in statistics, machine learning, and other fields. To solve

this problem, we introduce the Upper-Confidence Frank-Wolfe algorithm, inspired by techniques for

bandits and convex optimization. We show upper bounds on the optimization error of this algorithm

over various classes of functions, and discuss the optimality of these results.

3 - Including inexact second-order aspects in first-order methods for

nonconvex optimization

Clément Royer, Wisconsin Institute for Discovery, University of Wisconsin-Madison, USA, [email protected]

Co-author(s): Stephen Wright, Computer Sciences Department, University of Wisconsin-Madison, USA,

[email protected]

Abstract

First-order algorithms represent a popular class of techniques for solving smooth optimization

problems, either convex or nonconvex. One attractive feature of such frameworks is their low iteration

cost, essentially of order of one gradient evaluation. On the contrary, second-order methods can have a

prohibitive cost in large dimensions, due the linear algebra tailored to the Hessian matrix; still, using

second-order aspects may significantly improve the optimization process. In this talk, we describe a

line-search algorithm that incorporates inexact second-order information, in both a deterministic and a

stochastic sense. Our method is particularly suited to the nonconvex case, as we leverage randomized

linear algebra procedures to detect and exploit negative curvature. We provide a thorough complexity

analysis to assess the cost of our algorithm, as well as a numerical study to evaluate its practical

efficiency.

16:45 – 18:00

TC3 Optimization Theory

Contributed Session

Chair: Claudio Gentile Room: 6.2.48

1 - Bases of the subaditive cone and Benders decomposition for the dual of

the b-complementary multisemgroup problem

Eleazar Madriz, [email protected]

Abstract

An integer linear programming problem is an optimization problem in which all of the variables are

restricted to be integers. 1989 is the year of initiation the research into problems of linear linear

programming on algebraic. The Group Problem (GP) was defined by Gomory in 1968, the central idea

upon is based on the integer solution to a linear system of equations of an Integer Linear Problem can

be useful by transforming the system to an equation of elements in a finite abelian group. Aráoz (1972)

defines the Semigroup Problem (SP), these definitions include a Gomory's GP.

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Aráoz characterizes the polyhedra of the SP and show the relation between minimal system of linear

inequality of the polyhedra and extreme points and rays extreme of the dual of the SP. Ellis Johnson in

1980 considers dual of the master and the general semigroup problem. Aráoz and Johnson in 1982

present the Polyhedra of the multivalued additive system problem, these definitions include a

Gomory's GLIP and the ASP. Aráoz and Johnson (1989), they use bases of the subadditive cone to

characterize or define polyhedron system associated with Multivalued Additive System (MAS). This

result depends on knowing the base of the cone, but does not establish what happens to the system

for different bases. Thus, in this work, we demonstrate that for different bases of the cone the systems

that they define are equivalent.

A particular case of multivalued additive systems is the b-complementary Multisemigroup (b-cMs). In

general an b-cMs is an associative, abelian, b-consistent and b-complementary MAS. Madriz in 2016

constructs the dual problem associated with a b-cMs problem, extending the duality result of the 1980

semi-group by Johnson, in addition to proving the conditions for demonstrating the duality theorem for

this kind of combinatorial optimization problems. In general the dual problem is defined from a base of

the subadditive cone of the b-CMS problem, for this dual problem in this work we present the

decomposition of Benders.

In this work, we demonstrate the following theorem: We leave two bases of the subadditive cone

associated with a Linear Integer Linear Programming Problem b-Complementary Multisemigroup, the

systems defined by each base for the convex hull the solutions of the b-complementary

Multisemigroup Problem is equivalent. In addition, we present the dual problem associated with b-

CMLIP and the Benders decomposition of this dual problem.

2 - Bounds for ranks of polygons

António Goucha, Universidade de Coimbra, [email protected]

Co-author(s): João Gouveia, Universidade de Coimbra, [email protected]; Pedro M. Silva, Instituto Superior

Técnico, [email protected]

Abstract

Polytopes play a central role in optimization, namely because they are natural objects to represent a

wide variety of optimization problems. The difficulty of optimizing a linear function over a polytope

grows polynomially with its number of facets or vertices. A way to reduce the complexity is to replace a

polytope P by an extended formulation: a higher dimensional polytope Q such that PQproj )( for

some linear projection. The minimal number of facets of such Q is called the (linear) extension

complexity of P and is denoted )(Pxc . The extension Q might have exponentially less facets than P ,

which allows us to solve linear programs over P much more effectively.

To each polytope P one can associate a nonnegative matrix )(PS , called slack matrix of P , that

records the geometric information of the polytope. By a result of Yannakakis, it turns out that the

extension complexity of every polytope P is exactly the nonnegative rank of its slack matrix i.e., the

smallest k for which one can write )(PS as the sum of k nonnegative rank one matrices.

In this talk, we will study one of the simplest examples, that of n -gons. We introduce a new asymptotic

lower bound for the nonnegative rank of n -gons, which decreases the gap between the currently

known bounds, and we develop new upper bound for their boolean rank, a related factorization rank,

deriving from it some new numerical results and evidence for its asymptotic behavior.

Thursday Sessions


3 - Matrix decomposition and the perspective reformulation of

nonseparable quadratic programs

Claudio Gentile, IASI-CNR, [email protected]

Co-author(s): Antonio Frangioni, Dipartimento di Informatica - Università di Pisa, [email protected]; James

Hungerford, RaceTrac, Atlanta, Georgia, USA, [email protected]

Abstract

We are interested in solving Mixed-Integer Quadratic Programs (MIQP) with Semicontinuous Variables,

i.e. variables which may be either zero or belong to a compact set.

In Frangioni & Gentile (2006) Perspective Reformulation (PR) has been proved to be an effective tool to

solve MIQPs when the quadratic objective function is separable.

In Frangioni & Gentile (2007) and Zheng et al (2014) Semidefinite Programming methods have been

shown to be useful to extract a diagonal from the hessian matrix of the MIQP and to partial

reformulate the problem with the PR technique. Of course nonseparable hessian matrices can only be

approximated by diagonal matrices.

Here we study the problem of (approximately) decomposing the hessian matrix as the sum of positive

semidefinite matrices with a 2 × 2 nonzero structure. Solving this problem can enable the use of

Perspective Reformulation techniques for obtaining stronger lower bounds for MIQPs. We present two

exact SDP approaches for finding an approximate decomposition, we characterize the set of matrices

that have an exact decomposition, and we use the characterization to devise efficient heuristics for

obtaining 2 × 2 decompositions. We present preliminary results on the bound strength for Portfolio

Optimization problems, showing that for some classes of problems the use of 2 × 2 matrices can

significantly improve the quality of the bound w.r.t. the best previously known approach, although at a

possibly high computational cost.

16:45 – 18:00

TC4 Health Care Optimization

Contributed Session

Chair: Maria Eugénia Captivo Room: 6.2.47

1 - Optimizing ambulance dispatching and relocation using a

preparedness function

Ana Sofia Carvalho, CMAF-CIO, Universidade de Lisboa, [email protected]

Co-author(s): Maria Eugénia Captivo, CMAF-CIO Universidade de Lisboa, [email protected]; Inês Marques,

Instituto Superior Técnico, Universidade de Lisboa, CEG-IST, [email protected]

Abstract

Emergency Medical Service (EMS) aims to provide basic medical care for any person in an emergency

situation. Several resources, e.g. high specialized equipment and highly skilled staff are daily managed

and mobilized by EMS. Since the importance of having an effective and efficient EMS response is an

issue that concerns society, it is essential to have an optimized system. Through the years, since the

mid 1970’s, approaches which include exact methods, heuristic algorithms and simulation have been

Thursday Sessions


developed to include real life features in the problems that arise in this field of study.

In the EMS context, three levels of decision can be identified: strategic, tactical and operational. This

work focus on the operational level by solving the ambulance dispatching and relocation problem. Real-

time decisions in the operational level are very important in the EMS systems in order to reach each

emergency effectively facing dynamic changes at different time intervals. The main challenge is the

huge level of uncertainty involved namely in the emergencies’ demand and severity, ambulances'

availability, population density and traffic conditions.

Ambulance dispatching decisions assign ambulances to emergencies and the relocation problem calls

for the optimal redistribution of existing available ambulances. In order to have an efficient

management of the available resources it is very important to have a coverage measure to evaluate the

service's quality. We use a preparedness function which is commonly used in the EMS context.

Although being a simple measure, it is capable of securing a long-term efficiency, evaluating the future

ability of the system to handle potential emergencies.

These two problems are considered in a two-phase optimization approach. In the first phase the

dispatching problem is solved deciding which ambulances should be sent to each emergency using a

policy that sends the closest ambulance if the emergency is severe and uses the preparedness function

otherwise. Then, relocation decisions are taken namely where to relocate ambulances that have

finished the service and whether additional relocations between bases are needed. The preparedness

function is also incorporated in the second optimization phase.

The proposed approach aims to help managers in the decision-making process at the operational level

tasks. The Portuguese case of EMS where solving these problems has been a handmade task is used as

a case study.

2 - Comparison of different polices for multi-agent kidney exchange

programs

Xenia Klimentova, INESC TEC, [email protected]

Co-author(s): Nicolau Santos, Ana Viana, João Pedro Pedroso

Abstract

The kidney exchange problem arises in a framework of programs on living donation for patients who

have a donor willing to donate to him/her a kidney, but the pair is not physiologically compatible. Pairs

with these characteristics can be joined in a common pool to seek for possible exchanges between

them, when the donor from one pair can give a kidney to the patient from a second pair and,

backwards, the donor from the second pair donates to the patient from the first pair. The concept is

extended to cycles of size up to given parameter k .

Current widespread practice in kidney exchange programs is inclusion of altruistic donors - people

willing to donate one of their kidneys altruistically with no associated patient. When included in a

program an altruist initiates a chain where the last patient, so called bridge donor, usually donates to a

deceased donor waiting list, or acts as an altruistic donor in the next exchange. In European programs

chains are also considered to have limited length.

Most commonly the programs are aimed at maximization of the number of performed transplants, and

naturally formulated as a combinatorial optimization problem.

Recently Europe started discussion on organizing multi-country kidney exchange programs. Having

multiple agents in action, the question of ensuring equity for all agents involved is a key point for

designing such programs. Such equity may be jeopardized by the fact that, when maximizing the

Thursday Sessions


number of transplants, several optimal solutions may exist each of them benefiting one agent more

than others.

We study different policies to address such problem and ensure fairness between agents. The policies

keep track of the number of transplants performed by each country in each matching run and tries to

balance them over time, taking into account a fair value that it was expected to be achieved. As an

alternative to maximization of the number of transplants, we also consider the objective of minimizing

patients waiting time. The number of patients waiting for a given number of periods is maximized

lexicographically starting from the longest waiting time. Extensive computational experiments have

been performed for comparison of the proposed policies.

The work is financed by the European Regional Development Fund through the Operational

Programme for Competitiveness and Internationalisation – COMPETE 2020, by Portuguese funding

agency Fundação para a Ciência e a Tecnologia, projects "mKEP-Models and optimisation algorithms for

multicountry kidney exchange programs" (POCI-01-0145-FEDER-016677) and SFRH/BPD/101134/2014.

3 - Different perspectives for a surgical case assignment problem

Maria Eugénia Captivo, Centro de Matemática, Aplicações Fundamentais e Investigação Operacional, Faculdade de Ciências, Universidade de Lisboa, [email protected]

Co-author(s): Inês Marques, Centre for Management Studies, Instituto Superior Técnico, Universidade de Lisboa,

[email protected]

Abstract

The surgical suite has multiple and powerful stakeholders. In a public hospital, the government wants

to achieve some social measures like: number of patients in the waiting list, number of days in the

waiting list, or percentage of patients treated after the clinically acceptable period (maximum response

time). The administration of the hospital wants to achieve those goals in order to avoid high

contractual penalties; they also desire a high efficiency level of the surgical suite, not only because this

is a highly costly service with big influence in many other services in the hospital (e.g., wards) but also

because the number and complexity of the surgeries performed represent a significant hospital funding

source. At the same time, the surgeries are often scheduled by the surgeons depending on their

agenda and on their capacity to remember all of their patients. When a systematic system to select and

schedule the patients to be operated in a given week is not available, the surgeons will tend to select

the patients they remember the best (e.g. those patients more recently consulted or those patients

that pressure the surgeon). This can bring a sort of LIFO strategy to manage the waiting list for surgery,

which may undermine the government guidelines.

This work emerges from a close collaboration with a large and publicly funded Portuguese hospital. The

aim is to propose a systematic approach to help the surgical planner in the scheduling of elective

surgeries, in order to optimize the use of the available surgical resources and improve equity and

access to operated and waiting patients. The decisions to be taken are twofold: select patients to be

scheduled in the planning horizon from the large waiting list for surgery; and assign a day, an operating

room and a time block to the selected patients. We present different approaches that were developed

intending to mimic the different stakeholders’ perspectives for a surgical case assignment problem.

Results, using data from a Portuguese hospital, will be presented and discussed.

Thursday Sessions


16:45 – 18:00

TC5 Urban Transportation

Contributed Session

Chair: Marta Mesquita Room: 6.2.46

1 - A math-heuristic for bus driver rostering: generation, evolution and

repair

Vítor Barbosa, Instituto Politécnico de Setúbal, [email protected]

Co-author(s): Ana Respício, Universidade de Lisboa, [email protected]; Filipe Alvelos, Universidade do Minho,

[email protected]

Abstract

In this talk a new math-heuristic combining column generation and an evolutionary algorithm is

presented. The math-heuristic follows the concept of the framework “search by column generation”

and was developed to address a bus driver rostering problem, however, with small development, it can

be used with any other problem, provided that it can be represented by a decomposition model. The

bus driver rostering problem consists in defining the work-schedules for the drivers of a company for a

defined period, while ensuring the execution of the service and respecting the labour rules, enforced

by the company and the legislation, in the plan of each work-schedule. The objective is to optimize the

total labour cost.

In the talk the major components/stages of the math-heuristic will be presented applied to the bus

driver rostering problem: the generation of pools of valid work-schedules with column-generation, the

evolution of a population of rosters using an evolutionary algorithm and a repair operator designed to

restore invalid rosters. A decomposition for the bus driver rostering problem is presented, for which,

three distinct subproblem models and solvers were developed. Multiple column generation

configurations combining the models and solvers exist to obtain the search space for the metaheuristic

exploration.

An enhanced evolutionary algorithm, which is part of the extension of the original framework to allow

the use of population-based metaheuristics, is presented with details on the generation of global

solutions (rosters) considering as search-space the work-schedules obtained in the first stage. We also

present an additional operator embedded in the evolutionary algorithm, which repairs infeasible global

solutions and generates new subproblem solutions from within the search stage.

The approach was tested using three sets of instances for the bus driver rostering problem with

different number of drivers and duties. The results show that the proposed approach is effective in

achieving good quality solutions.

Results from the computational tests are presented. The results from the multiple configurations of the

column-generation stage are presented, followed by the evaluation of the integer solutions obtained by

the search with the evolutionary algorithm improved with the repair operator.

A comparison with integer solutions obtained by a commercial solver considering the compact model

of the problem, allowed us to conclude that, in general, the differences between both approaches are

small, except for the larger instances where the proposed math-heuristic obtains better solutions.

Thursday Sessions


2 - Multiple-period interval synchronization in urban public transport

Katarzyna Gdowska, AGH University of Science and Technology, Faculty of Management, Department of Operations research, Kraków, Poland, [email protected]

Abstract

The paper is devoted to multiple-period interval synchronization at long overlapping route segments of

an urban public transport network. Interval synchronization long overlapping route segments aims to

set departure time of every trip of every line, so that time gaps between arrivals of consecutive trips of

different lines at shared stops are equalized. Ride frequency of public transport is adjusted to

passenger flows, so it is a common practice to set different ride frequencies to smaller planning periods

(morning peak, valley hours, afternoon peak etc.). The duration of these periods does not have to be

equal. For each period headways, travel times and number of trips to be executed are specified with

respect to travel service needs. Discrepancies between headways adopted in consecutive periods affect

significantly departure times and – in consequence – arrival times at bus stops, so that the objective of

multiple-period interval synchronization is to smoothen transitions between periods, which means to

guarantee that every each line at every bus stop separation time between the last trip of the previous

period and the first trip of the next period has to fit some range. Multiple-period interval

synchronization in urban public transport can be formulated for a system with fixed or flexible

headways. Depending on the type of headway setting the range for separation time is different and it is

solved as a sub-problem. In this paper approaches to setting the range for separation time is

investigated. A multiple-criteria mixed-integer programming model for the multiple-period interval

synchronization problem is presented and results of computational experiments are reported.

3 - A decompose-and-fix heuristic for re-rostering bus drivers

Marta Mesquita, CMAF-CIO, ULisboa, ISA, [email protected]

Co-author(s): Margarida Moz, CMAF-CIO, ULisboa, ISEG, [email protected]; Ana Paias, CMAF-CIO, ULisboa,

FCUL, [email protected]; Margarida Pato, CMAF-CIO, ULisboa, ISEG, [email protected]

Abstract

The driver rostering problem in public transit companies aims at assigning daily crew duties to each

driver defining a sequence of workdays and days-off, the driver schedule, to be in force during a pre-

determined rostering horizon. A roster is the set of all driver schedules, together with the particular

work shifts that drivers must work on. Rosters must comply with Labor Law, unions’ agreements and

internal norms of the companies. These requirements are related to the minimum number of weekly

days-off; the minimum number of consecutive days-off; the number of days-off that must match with

weekend days; the maximum number of consecutive workdays; and the number of hours that drivers

must rest between two consecutive workdays. A driver cannot be assigned to an early duty if he

worked a late duty the day before. Moreover, it is desirable that a driver is not assigned to different

duty types on consecutive days. Therefore, in the first roster one considers the sequence early-late

infeasible. The objective is to minimize costs and to balance the workload among drivers.

During real-time control, absences of drivers call for adjustment in the current roster, the re-rostering

problem. Absent drivers must be substituted by reassigning daily crew duties to drivers, from the first

Thursday Sessions


day of drivers’ absences, eventually, until the end of the rostering horizon. The resulting new roster

should minimize the dissimilarities to the current roster so as to reduce the inconvenience of changing

the previously assigned schedules besides ensuring workload demand, rostering constraints and

maintaining the equilibrium of the roster.

In this talk, the re-rostering problem is formulated in a multilevel acyclic network through an integer

multi-commodity flow/assignment model. In the re-roster model changes in crew duties previously

assigned to drivers are penalized through the objective function. In order to use a reduced number of

“extra” drivers, the constraint that forbids the assignment of drivers to a sequence of early-late duties

becomes a soft constraint, within an adequate established time window.

Taking advantage from the network and model characteristics, a decompose-and-fix heuristic had been

developed to solve the rostering problem. This heuristic is now adapted to deal with the re-roster

model which recovers disruptions caused by unplanned absences of drivers.

Computational experience with instances derived from real world data is presented. Different scenarios

of disruptions are simulated and the resulting re-rostering solutions are analyzed.

16:45 – 18:00

TC6 Travelling Salesman Problem

Contributed Session

Chair: Daniel Santos Room: 6.2.45

1 - Models for the family traveling salesman problem

Raquel Bernardino, CMAF-CIO, Universidade de Lisboa, [email protected]

Co-author(s): Ana Paias, Universidade de Lisboa, [email protected], CMAF-CIO

Abstract

We will address the family traveling salesman problem (FTSP), which is a variant of the traveling

salesman problem (TSP). Given a depot and a set of cities, in the TSP the traveling salesman must find a

minimum cost route that visits all the cities, whereas in the FTSP the traveling salesman must also find

a minimum cost route but is only required to visit a predefined number of cities More formally, in the

FTSP the set of cities is partitioned into several subsets which are called families. The cost of traveling

between each pair of cities and between the depot and each city are known. The objective is to

determine a minimum cost route that: i) begins and ends at the depot; and ii) visits a given number of

cities in each family.

We propose three different models for the FTSP, one compact model and two non-compact ones. The

compact model uses flow variables disaggregated per family to ensure the route connectivity, this

model is called the family-commodity flow (FCF) model. One of the non-compact models uses an

adaptation of the known connectivity cuts for the FTSP to ensure that we will obtain a single connected

route as a solution. This model will be called the connectivity cuts (CC) model. The other non-compact

model is obtained through the FCF model and it is called the rounded family visits (RFV) model.

We were able to prove theoretically that, in terms of linear programming relaxation value, the non-

compact models outperform the compact one and they are not comparable with each other. With the

exact methods we were able to solve benchmark instances that have never been solved up to

optimality within a very reasonable computational time.

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Due to the FTSP being NP-hard (the TSP is a particular case) we also developed an iterated local search

(ILS) procedure to provide upper bounds for the instances that the exact methods could not solve. With

the ILS procedure we were able to improve the best known upper bounds from the literature in several

benchmark instances.

2 - New inequalities and formulations for the double TSP with multiple

stacks

Michele Barbato, DEIO, Faculdade de Ciências, Universidade de Lisboa, CMAF-CIO, [email protected]

Co-author(s): Luís Gouveia, DEIO, Faculdade de Ciências, Universidade de Lisboa, CMAF-CIO,[email protected];

Mathieu Lacroix, Laboratoire d'Informatique de Paris-Nord (LIPN) - Institut Galilée, Université Sorbonne Paris Cité

(USPC), [email protected]

Abstract

In the double TSP with multiple stacks (Petersen and Madsen (2009)), a vehicle with several stacks

performs a Hamiltonian circuit to pick up some items and stores them in its stacks. It then delivers each

item to a corresponding customer by performing a second Hamiltonian circuit. The stacks are subject to

a LIFO policy: only the items currently on the top of their stack can be delivered.

We observe that when in a feasible solution we fix enough consecutive sets of vertices in the pickup

and the delivery circuits, the order of the remaining vertices in the pickup circuit depends on the order

of the same vertices in the delivery circuit.

This observation lets us introduce several exponential-size families of so-called "block inequalities",

which are valid for an ILP formulation of the problem based on arc and precedence variables (Barbato

et al. (2016)).

We discuss separation procedures for these new inequalities. We then focus on the block inequalities

valid for the problem with two stacks: in this case, many of the families of block inequalities can be

separated in polynomial time and turn out to be effective in increasing the LP bound yielded by the

above-mentioned relaxation. We finally introduce a new formulation for the problem.

The new formulation still includes arc and precedence variables, as well as binary variables ),( jis

which are equal to one if and only if i and j are in the same stack. We explain how to revisit the block

inequalities for this formulation and we also introduce other strengthening inequalities for the new

model.

3 - A new formulation for the Hamiltonian p-median problem

Daniel Santos, CMAF-CIO, Universidade de Lisboa, [email protected]

Co-author(s): Tolga Bektaş, CORMSIS - University of Southampton, [email protected]; Luís Gouveia, CMAF-CIO,

Universidade de Lisboa, [email protected]

Abstract

We consider the Hamiltonian p -median problem on a directed graph, which consists of finding p

mutually disjoint circuits of minimum cost such that each node of the graph is included in one of the p

circuits. Recently proposed formulations are based on viewing the problem as resulting from the

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intersection of two subproblems, one stating that at most p circuits are required and another stating

that at least p circuits are required. Generalizations of the cut-set inequalities known from the

Traveling Salesman Problem model the former subproblem, while inequalities akin to path elimination

constraints for multi-depot routing problems model the latter. In this paper we present a new

formulation derived from a 3-layered graph that includes a new set of inequalities, namely multi-cut

constraints, that prevent solutions with less than p circuits. Some preliminary computational results

will be shown.

Friday Sessions


Friday

10:40 – 12:20

FA1 Recent Advances in First-Order Methods and Applications Organized Session

Organizer/Chair: Clément Royer Room: 6.2.50

1 - Iterative regularization for general inverse problems

Guillaume Garrigos, Istituto Italiano di Tecnologia, Italy, [email protected]

Co-author(s): Lorenzo Rosasco, Istituto Italiano di Tecnologia, Italy, [email protected]; Silvia Villa, Politecnico di

Milano, Italy, [email protected]

Abstract

In the context of linear inverse problems, we propose and study a general iterative regularization

method allowing to consider large classes of regularizers and data-fit terms. We propose for this an

algorithm, based on a primal-dual diagonal descent method, designed to solve hierarchical

optimization problems. Our analysis establishes convergence as well as stability results, in presence of

error in the data. In this noisy case, the number of iterations is shown to act as a regularization

parameter, which makes our algorithm an iterative regularization method.

2 - Activity identification and local linear convergence of forward-

backward-type methods

Jingwei Liang, DAMTP, University of Cambridge, UK, [email protected]

Co-author(s): Jalal Fadili, GREYC, CNRS, ENSICAEN, UNICAEN, France, [email protected]; Gabriel Peyré,

CNRS, DMA, ENS Paris, France, [email protected]

Abstract

In this talk, we consider the Forward--Backward splitting (a.k.a. proximal/projected gradient) algorithm

and its variants (inertial schemes, FISTA) for solving structured optimization problem. The goal of this

talk is to establish the local convergence rate analysis of this type of methods when the involved non-

smooth component of the problems partly smooth relative to an active manifold. We show that the

sequence generated by these methods will correctly identify the active manifolds in finite time, and

then enter a local linear convergence regime, which is characterize precisely based on the geometry of

the underlying smooth manifold. The obtained result is verified by several concrete numerical

experiments arising from compressed sensing, signal/image processing and machine learning.

Friday Sessions


3 - Scale-free texture segmentation

Nelly Pustelnik, Laboratoire de Physique, University of Lyon, ENS de Lyon, CNRS, Lyon, France, [email protected]

Co-author(s): Herwig Wendt, IRIT at INP-ENSEEIHT, University of Toulouse and CNRS, Toulouse, France,

[email protected]; Patrice Abry, Laboratoire de Physique, University of Lyon, ENS de Lyon, CNRS, Lyon, France,

[email protected]; Nicolas Dobigeon, IRIT at INP-ENSEEIHT, University of Toulouse and CNRS, Toulouse,

France, [email protected]

Abstract

Texture segmentation constitutes a standard image processing task, crucial for many applications. The

present contribution focuses on the particular subset of scale-free textures and its originality resides in

the combination of three key ingredients: First, texture characterization relies on the concept of local

regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as

wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off.

In nature, local regularity estimation shows high variability that impairs the detection of changes, while

a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the

present contribution proposes several variational problem formulations based on total variation and

proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation

performance for the proposed procedures are quantified and compared on synthetic as well as on real-

world textures.

4 - Accelerated alternating descent methods for Dykstra-like problems

Samuel Vaiter, CNRS & Université de Bourgogne, France, [email protected]

Co-author(s): Antonin Chambolle, CNRS & École Polytechnique, France,

[email protected]; Pauline Tan, École Polytechnique, France,

[email protected]

Abstract

In this talk, I will discuss our work extending recent results by A. Chambolle and T. Pock (ICG, TU Graz,

Austria) on the acceleration of alternating minimization techniques for quadratic plus nonsmooth

objectives depending on two variables. We discuss here the strongly convex situation, and how ``fast''

methods can be derived by adapting the overrelaxation strategy of Nesterov for projected gradient

descent. We also investigate slightly more general alternating descent methods, where several descent

steps in each variable are alternatively performed.


Friday Sessions


10:40 – 12:20

FA2 Mixed Integer Problems

Organized Session

Organizer/Chair: Agostinho Agra Room: 6.2.49

1 - Economic lot-sizing problem with remanufacturing option: complexity

and algorithms

Ashwin Arulselvan, Department of Management Science, University of Strathclyde, Glasgow, UK, [email protected]

Co-author(s): Kerem Akartunalı, Department of Management Science, University of Strathclyde, Glasgow, UK,

[email protected]

Abstract

In a single item dynamic lot-sizing problem, we are given a time horizon and demand for a single item

in every time period. The problem seeks a solution that determines how much to produce and carry at

each time period, so that we will incur the least amount of production and inventory cost. When the

remanufacturing option is included, the input comprises of number of returned products at each time

period that can be potentially remanufactured to satisfy the demands, where remanufacturing and

inventory costs are applicable. For this problem, we first show that it cannot have a fully polynomial

time approximation scheme (FPTAS). We then provide a pseudo-polynomial algorithm to solve the

problem and show how this algorithm can be adapted to solve it in polynomial time, when we make

certain realistic assumptions on the cost structure. We finally give a computational study for the

capacitated version of the problem and provide some valid inequalities and computational results that

indicate that they significantly improve the lower bound for a certain class of instances.

2 - Vehicle routing problem in wireless sensor networks

Luis Flores, Instituto de Matematicas y Ciencias Afines, Universidad Nacional de Ingenieria, Lima, Perú, [email protected]

Co-author(s): Rosa Figueiredo, Université d'Avignon, Avignon, France, [email protected];

E. Ocaña, Instituto de Matematicas y Ciencias Afines, Universidad Nacional de Ingenieria, Lima, Perú,

[email protected]

Abstract

The Vehicle Routing Problem (VRP) is one of the most extensively studied problems in operations

research due to its methodological interest and practical relevance in many fields such as

transportation, logistic, telecommunications, and production. In this work we have stations

represented by a set of nodes V and directed paths between stations represented by a set of arcs A .

Each node accumulates information that linearly depends on the time elapsed since the last extraction.

A base station Vs is the only point of communication with the outside. A vehicle based on the base

station is responsible for collecting the information from each node. Some stations sVVv \* can

transfer information to the vehicle by using wireless communication. In that case, the time for

transmission depends on the amount of information transmitted, the distance between nodes, and the

Friday Sessions


equipment installed in the receiving station information. The problem is to define routes for the vehicle

and also to decide how to collect the information in a way that the amount of information collected to

a finite time T is maximized. This problem can be seen as a special case of the Periodic VRP. We

propose 3 different objective functions to the problem and compare the solutions obtained.

3 - A decomposition algorithm for robust lot sizing problem with

remanufacturing option

Öykü Naz Attila, Department of Management Science, University of Strathclyde, Glasgow, UK, [email protected]

Co-author(s): Agostinho Agra, Department of Mathematics, University of Aveiro, [email protected]; Kerem Akartunali,

Department of Management Science, University of Strathclyde, Glasgow, UK, [email protected];

Ashwin Arulselvan, Department of Management Science, University of Strathclyde, Glasgow, UK,

[email protected]

Abstract

We propose a decomposition procedure for constructing robust optimal production plans for reverse

inventory systems, where deformed products that have been returned to the system (returns) are

restored to their usable state through remanufacturing. Our study is motivated by the need of

overcoming the excessive computational time requirements, as well as the inaccuracies caused by

imprecise representations of problem parameters. The present study aims to contribute to the growing

research on lot sizing problems with remanufacturing (ELSR), through implementing the robust

optimization framework to handle parameter uncertainties. We investigate the case when uncertainty

is imposed on the values of demands and returns, which are reformulated as parts of budgeted

polytopes. The robust ELSR problem is then solved using the decomposition algorithm, which solves a

restricted version of this robust ELSR problem (DMP) iteratively, with respect to the convex hulls of

partially enumerated extreme points of the uncertainty set. Given an optimal solution to DMP, we solve

a maximization problem, which seeks for values of demands and returns that maximises the total

inventory and backlogging costs for the given production plan (AP) . The solutions generated by AP are

used to update the partially enumerated extreme points, and the process is repeated until none of the

remaining extreme points worsen the latest production plan generated by DMP, where convergence is

guaranteed through the finiteness of the uncertainty sets. Finally, we perform a computational study

using our decomposition framework on several classes of computer generated test instances and we

report our experience.

4 - Policies for the robust lot-sizing problem with perishable products

Agostinho Agra, Department of Mathematics, University of Aveiro, [email protected]

Co-author(s): Marcio Santos, Université Libre de Bruxelles; Michael Poss, CNRS researcher in computer science,

LIRMM, Université de Montpellier, [email protected]

Abstract

Dealing with uncertainty is very important when solving practical lot-sizing problems where production

decisions need to be taken before the real demands are revealed. This issue is even more relevant

when products are perishable and significant costs can be originated from lost production due to an



Friday Sessions


overestimation of demands. We present a robust lot-sizing problem with recourse where the products

have a fixed shelf-life. The demands can be fulfilled by production in that period, from stock resulting

from production in the previous periods within the shelf-life, or backlogged. The quantities to produce

need to be decided in the beginning of the time horizon and the stock, the backlog and the lost

demand are adjusted to the scenario. We introduce a mathematical model for the nominal problem

and for the robust problem. In order to handle with the nonanticipativity constraints, two policies are

discussed. One based on the well-known affine decision rules and the other is a FIFO policy regarding

the use of the inventory. A decomposition algorithm is proposed and computational results are

presented. These results demonstrate the effectiveness of our approach and allow a comparison

between the two policies.

10:40 – 12:20

FA3 Routing II

Contributed Session

Chair: Germán Paredes-Belmar Room: 6.2.48

1 - Hybrid heuristic approaches for a stochastic production-inventory-

routing problem

Filipe Rodrigues, University of Aveiro and CIDMA, [email protected]

Co-author(s): Agostinho Agra, University of Aveiro and CIDMA, [email protected]; Cristina Requejo, University of Aveiro

and CIDMA, [email protected]

Abstract

We consider a stochastic single item production-inventory-routing problem with single producer and

multiple clients. Demands are considered uncertain and, at the clients, demand is allowed to be

backlogged incurring a penalty cost. A recourse model is presented where some decisions are taken

before the scenario is known, and the quantities to deliver to the clients and the inventory levels are

adjustable to the scenario. Valid inequalities are introduced to improve the stochastic model. As the

stochastic problem is quite difficult to solve, even for small size samples, the classical Sample

Approximation Approach (SAA) must be combined with efficient heuristics to generate the candidate

solutions. We propose an iterated local search (ILS) heuristic. In order to take advantage of the SAA, we

propose a new heuristic procedure, called Adjustable Sample Approximation Approach that combines

ideas from the SAA and from relax-and-fix approaches. Tests based on randomly generated instances

are reported showing that the new Adjustable SAA performs better than the classical SAA and the SAA

combined with the ILS heuristic for hard instances.


Friday Sessions


2 - An iterative optimization approach for drone supported travelling

salesman problem

Emine Es Yurek, Department of Industrial Engineering, Uludag University, Bursa, Turkey, [email protected]

Co-author(s): H. Cenk Ozmutlu, Department of Industrial Engineering, Uludag University, Bursa, Turkey,

[email protected]

Abstract

Efficiency of delivery operations has critical importance in logistics and e-commerce sector. It is

common to use delivery trucks in last-mile delivery; however, they are not fast enough. As a result of

this, companies look for new approaches to reduce delivery times. An emerging concept, which is a

new variant of travelling salesman problem, proposes deploying drone as well as traditional delivery

truck. Drone is considered to complement the truck because of its features in contrast to disadvantages

of truck such as low speed and congestion. Since some commercial firms announced that they have

already begun to test using drones in delivery, it needs to be investigated from the view of operational

efficiency. This study presents an iterative algorithm based on decomposition approach to solve drone

supported travelling salesman problem with the purpose of minimum delivery completion time. We

determine customer assignments to each vehicle first and optimize routing decisions in the second

stage. To reduce computational efforts, we also fix the truck route in the first stage. Thus, in the second

stage, we solve a mixed-integer linear programming formulation to optimize drone route. The proposed

algorithm is compared with the previously developed models and the results demonstrate that our

algorithm gives shorter solution times.

3 - Utilization of internet of things for routing in city logistics

Katarzyna Gdowska, INESC TEC, Centre for Industrial Engineering and Management, Porto, Portugal; AGH University of Science and Technology, Faculty of Management, Department of Operations Research, Kraków, Poland, [email protected]

Abstract

The paper is devoted to routing problem in city logistics. Growing share of e-commence results in a

surge in home deliveries provided by professional delivery companies. Due to the relatively big number

of unattended deliveries, some parcels have to be delivered repeatedly several times what affect fleet

routing and result in increasing delivery costs. City sprawl also contributes in the increase of traffic

congestion, as people living in suburbs commute every day to work in the city center. Improving

sustainability of urban freight systems and passenger transport is one of the needs faced by current

and future cities, since the social and environmental costs of city logistics are huge. The Internet of

Things can provide tools for re-organizing routing of delivery freight and passenger transport in more

efficient manner. The number of home deliveries performed by professional fleet can be reduced by

introducing deliveries to modular parcel stations, where a parcel can wait for being picked-up by the

final customer. More dynamic approach is based on mobile delivery addresses tracked by delivery

company. Another possibility is provided by crowdsourcing – involving crowd into performing delivery

service. Use of the Internet of Things can also enhance routing in passenger transport – park&ride

system integrated with public transport, carsharing and carpooling. In this paper results of a simulation

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of a city logistics system is presented – professional freight routing is enhanced with above-mentioned

methods. Obtained results are compared to the ones achieved for a vehicle routing problem with time

windows and areas for possible optimization are indicated.

4 - The HAZMAT distribution problem with multiple products

Germán Paredes-Belmar, Universidad Andres Bello, [email protected]

Co-author(s): Aldo Espinoza, Universidad Andres Bello, [email protected]; Andrés Bronfman,

Universidad Andres Bello, [email protected]

Abstract

We present a new HAZMAT distribution problem with multiple products, in which a set of hazardous

materials are distributed to a set of customers using a truck fleet. The different materials can be

distributed and combined on a same truck.

Each truck leaves and returns to the depot once materials are delivered. The population exposed to an

accident has a different type of exposure, depending on the product combination in a truck. The risk to

which the population is exposed by a truck shipment can changes when a new type of material with

different risk is delivered to a customer. Naturally, the dangerous materials tend to be delivered first, to

reduce the exposed population of the truck routes, but it may generate high transportation costs.

Furthermore, we consider the incompatibilities between different types of materials. Using a bi-

objective integer programming model, we minimize the total population exposure and transportation

costs. We present a case study in the city of Santiago of Chile to show the practical application of our

proposed approach.

10:40 – 12:20

FA4 Networks II

Contributed Session

Chair: Dalila B. M. M. Fontes Room: 6.2.47

1 - Robustness assessment of complex networks based on the Kirchhoff

index

Alessandra Cornaro, Departement of Mathematics, Catholic University of Milan, [email protected]

Co-author(s): Monica Bianchi, Departement of Mathematics, Catholic University of Milan,

[email protected]; Gian Paolo Clemente, Departement of Mathematics, Catholic University of Milan,

[email protected]; Anna Torriero, Departement of Mathematics, Catholic University of Milan,

[email protected]

Abstract

This paper is aimed to the inspection of a graph measure called effective graph resistance, also known

as Kirchhoff index (or resistance distance), derived from the field of electric circuit analysis. It is defined

as the accumulated effective resistance between all pairs of vertices. This index is widely used in



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Mathematical Chemistry, Computational Biology and, more generally in Network Analysis in order to

describe the graph topology. The objective of the paper is twofold. First, we survey known results

regarding the Kirchhoff index and we discuss a methodology in order to obtain some new and tighter

bounds of this graph invariant. The derivation of these new limitations takes advantage of real analysis

techniques, based on majorization theory and optimization of functions which preserve the

majorization order, the so-called Schur-convex functions. Secondly, we focus on the application of this

topological index in the analysis of robustness-related problems. It is worth pointing out that the

Kirchhoff index can be highly valuable and informative as a robustness measure of a network, showing

the ability of a network to continue performing well when it is subject to failure and/or attack. In fact,

the pairwise effective resistance measures the vulnerability of a connection between a pair of vertices

that considers both the number of paths between the vertices and their length. A small value of the

effective graph resistance therefore indicates a robust network. Being the calculation of the exact value

of the Kirchhoff index computationally intensive, bounds on this graph invariant have been also

proposed in the literature as an alternative measure of robustness. In particular, the fact that the

Kirchhoff index can be also expressed by the Laplacian eigenvalues, entails a relation with the algebraic

connectivity, that is often applied as a useful approximation to assess robustness. However, it has been

shown that the algebraic connectivity may not display desirable properties for a robustness indicator.

Within this topological robustness framework, we propose to use our bounds, obtained via

majorization techniques, for robustness assessment of complex networks. A comparison with

alternative graph measures is provided by applying our methodology to random network models and

real networks. Further research could regard a generalization to weighted and/or directed networks

and the analysis of the correlation between alternative topological metrics.

2 - Locating a cluster head for minimum-power under symmetric range

assignment

Kevin Prendergast, Department of Mechanical Engineering, University of Melbourne, Australia, [email protected]

Co-author(s): Charl Ras, Department of Mathematics and Statistics, University of Melbourne, Australia,

[email protected]; Doreen Thomas, Department of Mechanical Engineering, University of Melbourne,

Australia, [email protected]

Abstract

For a star network consisting of a given set of nodes on a Euclidean plane and a master node at the star

point, the object is to optimise the location of the star point. This unconstrained convex optimisation is

with respect to the power required by the network. The power required by a node is a quadratic

function of its distance to the master node, and for the master node is a quadratic function of its

distance to a farthest node. The power required by the network is the sum of the requirements of the

nodes and the master node. The optimised location of the star point, that which minimises the power

requirement of the network, is defined as the min-power centre. The sum of the quadratic functions is

a strictly convex function, which ensures that there is only one min-power centre. The optimisation

process begins at the centroid of the given set of nodes, where we establish a star point that we will

move in the direction that produces the maximum rate of reduction of the value of the power function.

It turns out that the optimisation path of the star point that provides the maximum rate of decrease in

the value of the power function is a series chain of straight edges which lie on the perpendicular

bisectors of edges joining particular pairs of nodes. The path terminates at the min-power centre,

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which is the point of convexity of the power function, or is the centre of a circle on which lie three

nodes, including a node farthest from the centroid, that form an acute angled triangle. In the latter

case no further decrease is possible. Algebraic characterisations of the possible min-power centres for

different given sets of nodes, and associated values of the power function, are provided.

3 - Heuristics solutions for the maximum edge weight clique problem: a

quadratic approach

Dalila B.M.M. Fontes, Universidade do Porto, [email protected]

Co-author(s): Seyedmohammadhossein Hosseinian, Texas A&M University, Department of Industrial and Systems

Engineering, United States, [email protected]; Sergiy Butenko, Texas A&M University, Department of Industrial

and Systems Engineering, United States, [email protected]

Abstract

This work addresses the maximum edge weight clique problem, a generalization of the well-known

maximum clique problem.

We propose to address this problem by resorting to a quadratic discrete formulation. This is then

converted into an equivalent quadratic continuous formulation, from which a heuristic approach is

derived based on the optimization of a quadratic function over a sphere.

Preliminary computational results are reported for a subset of benchmark problem instances derived

from the DIMACS maximum clique instances.

Acknowledgments: We acknowledge the financial support of "NORTE-01-0145-FEDER-000020",

financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL

2020 Partnership Agreement.

10:40 – 12:20

FA5 Optimization Applications Contributed Session

Chair: Abílio Lucena Room: 6.2.46

1 - Directed clustering in weighted networks: a new perspective

Gian Paolo Clemente, Catholic University, Department of Mathematics, Milan, Italy, [email protected]

Co-author(s): Rosanna Grassi, University of Milano-Bicocca, Dept. of Statistics, [email protected]

Abstract

Modelling complex systems by means of network theory is a common approach in different fields.

Many studies focus on binary undirected networks. Although such networks allowed to properly model

various real-world phenomena, further complexity is often needed to adequately catch heterogeneous

strengths and asymmetric connections between pairs of nodes. In these contexts, weighted and

Friday Sessions


directed networks are fruitful tools. Several topological properties of networks have been identified as

useful indicators which enhance the efficiency of a network in carrying out its essential functionality.

Among these is the case of clustering coefficient that measures the tendency to which nodes in a graph

tend to cluster together. Indeed, in most real networks empirical evidence shows that nodes tend to

form tightly-knit groups characterized by a relatively high density of ties. Referring to binary undirected

networks, two definitions of clustering coefficient have been proposed in the literature from two

different views. At global level, the transitivity gives an overall indication of the clustering in the

network. At local level, the coefficient quantifies how close the node’s neighbours are to being a clique.

Although the local coefficient suffers from a number of limitations, it is capable to capture the degree

of social embeddedness of single nodes and it is used by several mainstream indicators to assess

specific properties of a network. In this paper we consider the problem of assessing local clustering in

weighted directed networks. The generalization to weighted directed networks is indeed a crucial step,

because real-world networks often involve both asymmetric and weighted relationships. Various

generalizations to weighted networks have been proposed, whereas the weighted and asymmetric case

has received less attention up to now and the most significant contribute to this issue can be found in

Fagiolo (2007). However, this coefficient does not involve the strength of the node in the normalization

factor, leading to inconsistent results when the skewness of weight link distribution increases. We

propose a new clustering coefficient for weighted and directed networks by generalizing the coefficient

proposed in Barrat (2004). Since in directed networks edges pointing in different directions should be

interpreted differently, we define, as in Fagiolo (2007), a specific clustering coefficient considering

separately different edge patterns from a node perspective. Main concepts supported by empirical

experiments on several real networks belonging to different field. The performance of this new

definition is compared with that of existing coefficients in the literature.

2 - Genetic algorithm for intrusion detection of pervasive and ubiquitous

environments

Lynda Sellami, University of Bejaia, [email protected]

Co-author(s): Djilali Idoughi, LMA Laboratory, University of Bejaia, [email protected]

Abstract

Ubiquitous computing applies to all domains, with its sensitivity to context, invisibility and mobility.

Ubiquitous objective is improve the lives of men (people) in making their service, and facilitate access

to information at all times, assuring comfort, safety and/or assistance in the daily activities of people.

One of its important characteristic is its availability at all times, which makes them vulnerable to

security attacks; hence the need for individuals and organizations to protect their assets and / or

systems against theft and protect their privacy from intrusions. Intrusion is abuses by intruders for the

purpose of procuring information or acquiring services and other forms of abuse. Finding and

correcting all these defects is very important to ensure the proper functioning of the system. To better

protect and manage intrusion, intrusion detection systems (IDS) are widely used as protection tools.

The purpose of an intrusion detection system is to extract and classify relevant data from a wide variety

of data. To address the new vulnerabilities introduced by ubiquitous computing, security and privacy

guarantees in ubiquitous computing environments, we propose an IDS based on genetic algorithm (GA)

in order to protect the ubiquitous environments.

Friday Sessions


3 - On the dynamics of computer viruses transmission using an

epidemiological approach

M. Teresa T. Monteiro, Algoritmi R&D Center and Department of Production and Systems, University of Minho, Braga, Portugal, [email protected]

Co-author(s): João N.C. Gonçalves, Algoritmi R&D Center and Department of Production and Systems, University of

Minho, Braga, Portugal, [email protected]; Helena Sofia Rodrigues, School of Business Studies,

Polytechnic Institute of Viana do Castelo, Valença, Portugal and Center for Research and Development in

Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal,

[email protected]

Abstract

Over time, in order to try to understand the dynamics of computer viruses transmission and minimize

its propagation within network systems, epidemiological models began being intensively explored. In

this context, by taking advantage of a recent R package for Mathematical Modeling of Infectious

Diseases, the propagation of malicious objects within a computer network system is modeled and

illustrated using SIS (Susceptible-Infected- Susceptible) and SIR (Susceptible-Infected-Recovered)

epidemic models. In addition, a control strategy to minimize the propagation of virus infections is

studied and discussed, using real numerical data from real malware attacks.

4 - Analytical models to estimate connectivity and value in the

international trade of supplies

Abilio Lucena, Federal University of Rio de Janeiro, [email protected]

Co-author(s): Diogo Braga, Federal Fluminense University, [email protected]; Joaquim Guilhoto,

University of São Paulo, [email protected]

Abstract

The production of goods and services were dramatically changed over the last thirty years by what

became known as Global Value Chains (GVCs). Driven by a sharp reduction in transportation and

telecommunication costs and aimed at reducing their overall costs, companies started to include third

countries in their production processes. Accordingly, instead of simply relying upon eventual

advantages offered by local manufacturing, they started to split production among a network of

international partners. From the production of the simplest components to the assembling of an entire

product, such a fragmentation of production relied mostly on the availability of cheaper labor

elsewhere, at least initially. Furthermore, as it progressed, it changed the notion of competitiveness,

pushing it from the local sphere to the regional and global ones.

As suggested by many authors, the inflexion point for international supply-chain trade occurred in the

mid 1980's and was brought about mostly by Asian countries such as Japan, China and South Korea. To

a lesser extent, other countries also contributed to that trend. That is the case, for instance, of those

geographically dispersed developing countries that softened their protectionist economic policies

during the 1990's and early 2000's so as to take full advantage of production sharing opportunities.

Mexico, with the Maquiladora initiative, is a good example of that.

This paper is focused on assessing and quantifying international supply-chain trades at the regional and



Friday Sessions


global levels. To that order it introduces a new analytical tool that relies on the intuitive notion that, as

GVCs are established, the connections they forge among economic sectors from different countries

may eventually lead to the establishment of additional GVCs. Accordingly, larger and larger clusters of

highly interconnected and somewhat complementary sectors would thus result. We call these clusters

Trade Cliques (TCs) and envisage them as the backbones over which GVCs operate.

In this investigation, we identify TCs with the highest possible monetary value for the world economy

and also for the regional economies of Asia, Europe, and North America. The data we use to accomplish

that objective originates from WIOD input-output tables. Once a TC is identified, a cluster of key

economic sectors is then uncovered for it. To identify a TC and its corresponding cluster of key

economic sectors, two distinct Combinatorial Optimization problems are solved. One of them is well

known in the literature while the other is introduced in this paper.

Indices

Indices


Authors Index

A Abry, Patrice . . . . . . . . . . . . . . . . . . . . . 116

Adakoy, Elif . . . . . . . . . . . . . . . . . . . . . . 86

Agra, Agostinho . . . . . . . . . . . 51, 118, 119

Akartunali, Kerem . . . . . . . . . . . . 117, 118

Albareda-Sambola, Maria . . . . . . . . . . 79

Alessandretti, Andrea . . . . . . . . . . . . . 53

Almeida, Maria Teresa . . . . . . . . . . . . 83

Alvelos, Filipe . . . . . . . . . . . . . . . . . . . . 110

Amaral, Paula . . . . . . . . . . . . . . . . . . . 103

Aneja, Yash . . . . . . . . . . . . . . . . . . . . . . 67

Anjos, Miguel . . . . . . . . . . . . . . . . . . . . 100

Antunes, António . . . . . . . . . . . . . 76, 91

Arulselvan, Ashwin . . . . . . . . . . . . 117, 118

Arya, Rubi . . . . . . . . . . . . . . . . . . . . . . . 57

Atamna, Asma . . . . . . . . . . . . . . . . . . . 54

Attila, Öykü Naz . . . . . . . . . . . . . . . . . . 118

Auger, Anne . . . . . . . . . . . . . . . . . . . 54, 97

Aydin, Seckin . . . . . . . . . . . . . . . . . . . . . 84

B Bach, Francis . . . . . . . . . . . . . . . . . . . . . 104

Bandeira, Daniel . . . . . . . . . . . . . . . . . . 58

Bandeira, Luís Miguel . . . . . . . . . . . . . . 90

Bao, Tang Quoc . . . . . . . . . . . . . . . . . . . 97

Barbarosie, Cristian . . . . . . . . . . . . . . . . 54

Barbato, Michele . . . . . . . . . . . . . . . . . . 113

Barbosa, Vitor . . . . . . . . . . . . . . . . . . . . 110

Basto, João . . . . . . . . . . . . . . . . . . . . . . . 68

Bektaş, Tolga . . . . . . . . . . . . . . . . . . . . . 113

Benati, Stefano . . . . . . . . . . . . . . . . . . . 99

Benavent, Enrique . . . . . . . . . . . . . . . . . 63

Bergou, El Houcine . . . . . . . . . . . . . . . . 65

Bernardino, Raquel . . . . . . . . . . . . . . . . 112

Bernardo, Marcella . . . . . . . . . . . . . . . . 63

Berthet, Quentin . . . . . . . . . . . . . . . . . . 104

Bianchi, Monica . . . . . . . . . . . . . . . . . . . 121 Bomze, Immanuel . . . . . . . . . . . 44, 92, 103

Borsani, Ignacio . . . . . . . . . . . . . . . . . . . 58

Bostanabad, Mina Saee . . . . . . . . . . . . 80

Braga, Diogo . . . . . . . . . . . . . . . . . . . . . 125

Brandão, Susana . . . . . . . . . . . . . . . . . . 75

Brás, Carmo P. . . . . . . . . . . . . . . . . . . . . 69

Brás, Raul . . . . . . . . . . . . . . . . . . . . . . . . 83

Brockhoff, Dimo . . . . . . . . . . . . . . . . 54, 97

Bronfman, Andrés . . . . . . . . . . . . . . . . . 121

Butenko, Sergiy . . . . . . . . . . . . . . . . . . . 123

Büskens, Christof . . . . . . . . . . . . . . . . . . 64

C Cabezas, Xavier . . . . . . . . . . . . . . . . . . . 78

Campi, Marco . . . . . . . . . . . . . . . . . . . . 43

Captivo, Maria Eugénia . . . . . . . . 107, 109

Cardoso, Domingos M. . . . . . . . . . . . . . 95

Carvalho, Ana Sofia . . . . . . . . . . . . . 75, 107

Carvalho, Filipa Duarte de . . . . . . . . . . 81

Carvalho, Paula . . . . . . . . . . . . . . . . . . . 95

Casado, Leocadio G. . . . . . . . . . . . . . . . 103

Castillo, Pedro A. Castillo . . . . . . . . . . . 88

Castro, Joana . . . . . . . . . . . . . . . . . . . . . 76

Castro, Pedro . . . . . . . . . . . . . . . . . . . . . 88

Cavadas, Joana . . . . . . . . . . . . . . . . . 76, 91

Cerdeira, Jorge Orestes . . . . . . . . . . . . . 94

Chambolle, Antonin . . . . . . . . . . . . . . . 116

Chandrasekaran, R. . . . . . . . . . . . . . . . . 67

Chen, Dehan . . . . . . . . . . . . . . . . . . . . . 96

Cheng, Wenming . . . . . . . . . . . . . . . . . . 75

Christof, Constantin . . . . . . . . . . . . . . . . 72

Clason, Christian . . . . . . . . . . . . . . . 72, 96

Clemente, Gian Paolo . . . . . . . . . . 121, 123

Constantino, Miguel . . . . . . . . . . . . . . . 86

Corberan, Àngel . . . . . . . . . . . . . . . . . . . 63

Cornaro, Alessandra . . . . . . . . . . . . . . . 121

Correia, Isabel . . . . . . . . . . . . . . . . . . . . 101

Custódio, Ana Luísa . . . . . . . . . . . . . . . . 98

D De Mauri, Massimo . . . . . . . . . . . . . . . . 87

Delot, Thierry . . . . . . . . . . . . . . . . . . . . . 91

Désidéri, Jean-Antoine . . . . . . . . . . . . . . 55

Dobigeon, Nicolas . . . . . . . . . . . . . . . . . 116

Duijkeren, Niels van . . . . . . . . . . . . . . . . 53

Dür, Mirjam . . . . . . . . . . . . . . . . . . . . . . 102

I Indices


E El Cadi, Abdessamad Ait . . . . . . . . . . . . . . 91

El Amri, Mohamed Reda . . . . . . . . . . . . . . 66

Engel, Sebastian . . . . . . . . . . . . . . . . . . . . 72

Espinoza, Aldo . . . . . . . . . . . . . . . . . . . . . . 121

F Fadili, Jalal . . . . . . . . . . . . . . . . . . . . . . . . 115

Farid, Mahboubeh . . . . . . . . . . . . . . . . . . 74

Faulwasser, Timm . . . . . . . . . . . . . . . . . . 53

Fernández, Elena . . . . . . . . . . . . . . . . . . . 79

Fernandez, Pascual . . . . . . . . . . . . . . . . . 101

Ferreira, José Soeiro . . . . . . . . . . . . . . 68, 90

Figueiredo, Rosa . . . . . . . . . . . . . . . . . . . . 117

Filipecki, Bartosz . . . . . . . . . . . . . . . . . . . . 83

Fischer, Andreas . . . . . . . . . . . . . . . . . . . . 69

Flores, Luís . . . . . . . . . . . . . . . . . . . . . . . . 117

Fonseca, Maria da Conceição . . . . . . . . . 85

Fontes, Dalila B. M. M. . . . . . . . . . . . . 67, 123

Fontes, Fernando . . . . . . . . . . . . . . . . 54, 64

Fortz, Bernard . . . . . . . . . . . . . . . . . . . . . . 71

Frangioni, Antonio . . . . . . . . . . . . . . . . . . . 107

G

Gabl, Markus . . . . . . . . . . . . . . . . . . . . . . . 93

Gamarra, Luis Francisco Castillo . . . . . . . 58

Garmanjani, Rohollah . . . . . . . . . . . . . . . 74

García, Sergio . . . . . . . . . . . . . . . . . . 78, 99

Garrigos, Guillaume . . . . . . . . . . . . . . . . . 115

Gdowska, Katarzyna . . . . . . . . . . . . 111, 120

Gendron, Bernard . . . . . . . . . . . . . . . . . . . 71

Gentile, Claudio . . . . . . . . . . . . . . . . . . . . 107

Goksu, Gokhan . . . . . . . . . . . . . . . . . . . . . 86

Goldfarb, Donald . . . . . . . . . . . . . . . . . . . 47

Gonçalves, Graça . . . . . . . . . . . . . . . . . . . 100

Gonçalves, João N. C. . . . . . . . . . . . . . . . . 125

Gondzio, Jacek . . . . . . . . . . . . . . . . . . . . . 46

Gonzalez, Juan José Salazar . . . . . . . . . . . 52

Goucha, António . . . . . . . . . . . . . . . . . . . . 106

Gouveia, João Eduardo da Silveira 80, 106

Gouveia, Luís . . . . . . . . . . . . . 61, 62, 70, 113

Gower, Robert M. . . . . . . . . . . . . . . . . . . 104

Grassi, Rosanna . . . . . . . . . . . . . . . . . . . . 123

Gratton, Serge . . . . . . . . . . . . . . . . . . . . . 66

Groetzner, Patrick . . . . . . . . . . . . . . . . . . . 102

Guerriero, Francesca . . . . . . . . . . . . . . . . 57

Guilhoto, Joaquim . . . . . . . . . . . . . . . . . . 125

Guo, Peng . . . . . . . . . . . . . . . . . . . . . . . . . 75

H

Hansen, Nikolaus . . . . . . . . . . . . . . . . 54, 97

Helbert, Céline . . . . . . . . . . . . . . . . . . . . . 66

Hendrix, Eligius M. T. . . . . . . . . . . . . . . . . 103

Herrich, Markus . . . . . . . . . . . . . . . . . . . . 69

Homayouni, Seyed Mahdi . . . . . . . . . . . . 67

Hosseini, Seyedehsomayeh . . . . . . . . 73, 123

Hungerford, James . . . . . . . . . . . . . . . . . . 107

I Idoughi, Djilali . . . . . . . . . . . . . . . . . . . . . 124

Iglésias, Carlos . . . . . . . . . . . . . . . . . . . . . 75

Ishida, Hideshi . . . . . . . . . . . . . . . . . . . . . 60

J Jarboui, Bassem . . . . . . . . . . . . . . . . . . . . 91

Josz, Cédric . . . . . . . . . . . . . . . . . . . . . . . . 81

Joyce-Moniz, Martim . . . . . . . . . . . . . . . 70

Júdice, Joaquim J. . . . . . . . . . . . . . . . . . . 69

K Kahr, Michael . . . . . . . . . . . . . . . . . . . . . . 92

Kalayci, Can B. . . . . . . . . . . . . . . . . . . . . . 84

Kalcsics, Joerg . . . . . . . . . . . . . . . . . . . . . 89

Kawahara, Genta . . . . . . . . . . . . . . . . . . 60

Klimentova, Xenia . . . . . . . . . . . . . . . . . . 108

Knauer, Matthias . . . . . . . . . . . . . . . . . . . 64

Kostyukova, Olga . . . . . . . . . . . . . . . . . . . 81

Kruse, Florian . . . . . . . . . . . . . . . . . . . . . 96

Kunisch, Karl . . . . . . . . . . . . . . . . . . . . 72, 96

Kurdina, Maria . . . . . . . . . . . . . . . . . . . . . 81

L Labbé, Martine . . . . . . . . . . . . . . . . . . . . . . 41

Lacroix, Mathieu . . . . . . . . . . . . . . . . . . . . . 113

Laganà, Demetrio . . . . . . . . . . . . . . . . . . . . 63

Lancinskas, Algirdas . . . . . . . . . . . . . . . . . . 101

Leitner, Markus . . . . . . . . . . . . 61, 62, 70, 92

Indices


Lepreux, Olivier . . . . . . . . . . . . . . . . . . . 66

Letchford, Adam . . . . . . . . . . . . . . . . . . 52

Li, Xiangyong . . . . . . . . . . . . . . . . . . . . . 67

Liang, Jingwei . . . . . . . . . . . . . . . . . . . . 115

Ljubic, Ivana . . . . . . . . . . . . . . . . . . . 62, 78

Lopes, Sérgio . . . . . . . . . . . . . . . . . . . . . 55

Lourenço, Lídia . . . . . . . . . . . . . . . . . . . 100

Lucena, Abilio . . . . . . . . . . . . . . . . . . . . 125

Luipersbeck, Martin . . . . . . . . . . . . . . . 62

Lupuleac, Sergey . . . . . . . . . . . . . . . . . . 60

Luz, Carlos J. . . . . . . . . . . . . . . . . . . . . . 95

M

Madeira, José Aguilar . . . . . . . . . . . . . . 98

Madriz, Eleazar . . . . . . . . . . . . . . . . . . . 105

Madsen, Henrik . . . . . . . . . . . . . . . . . . 74

Mahalec, Vladimir . . . . . . . . . . . . . . . . . 88

Mahjoub, Ridha . . . . . . . . . . . . . . . . . . 52

Marques, Inês . . . . . . . . . . . . . . . . 107, 109

Martins, Carlos . . . . . . . . . . . . . . . . . . . 85

Martins, Pedro . . . . . . . . . . . . . . . . . . . 51

Melo, Rafael . . . . . . . . . . . . . . . . . . . . . 82

Melo, Teresa . . . . . . . . . . . . . . . . . . . . . 101

Menezes, Mozart B. C. . . . . . . . . . . . . . 79

Mercier, Quentin . . . . . . . . . . . . . . . . . 55

Mesquita, Marta . . . . . . . . . . . . . . . . . . 111

Messine, Frederic . . . . . . . . . . . . . . . 59, 88

Meyer, Christian . . . . . . . . . . . . . . . . . 72, 73

Molzahn, Daniel . . . . . . . . . . . . . . . . . . 81

Monteiro, M. Teresa T. . . . . . . . . . . . . . 125

Moreno, Eduardo . . . . . . . . . . . . . . . . . 78

Mourão, Maria Cândida . . . . . . . . . . . . 86

Moz, Margarida . . . . . . . . . . . . . . . . . . . 111

Mumcuoglu, Melis . . . . . . . . . . . . . . . . 86

O

Ocaña, E. . . . . . . . . . . . . . . . . . . . . . . . . 117

Okema, Chiharu . . . . . . . . . . . . . . . . . . 60

Orucoglu, Kamil . . . . . . . . . . . . . . . . . . 86

Ozmutlu, H. Cenk . . . . . . . . . . . . . . . . . 120

P

Paias, Ana . . . . . . . . . . . . . . . . . . . 111, 112

Paiva, Luís Tiago . . . . . . . . . . . . . . . . . . 64

Pannek, Jürgen . . . . . . . . . . . . . . . . . . . 63

Paredes-Belmar, Germán . . . . . . . . . . . 121

Pascoal, Marta . . . . . . . . . . . . . . . . . . . 58

Pato, Margarida . . . . . . . . . . . . . . 85, 111

Patricio, Pedro . . . . . . . . . . . . . . . . . . . . 70

Pawuels, Benoît . . . . . . . . . . . . . . . . . . 66

Pedroso, João Pedro . . . . . . . . . . . . . . . 108

Pehlivan, Nimet Yapici . . . . . . . . . . . . . 56

Pelegrin, Blas . . . . . . . . . . . . . . . . . . . . . 101

Perchet, Vianney . . . . . . . . . . . . . . . . . . 104

Pereira, Sérgio . . . . . . . . . . . . . . . . . . . . 56

Pesneau, Pierre . . . . . . . . . . . . . . . . . . . 62

Petukhova, Margarita . . . . . . . . . . . . . . 60

Peyré, Gabriel . . . . . . . . . . . . . . . . . . . . 115

Phusingha, Saranthorn . . . . . . . . . . . . . 89

Pieper, Konstantin . . . . . . . . . . . . . . . . . 97

Pinto, Leonor S. . . . . . . . . . . . . . . . . . . . 86

Pipeleers, Goele . . . . . . . . . . . . . . . . 53, 87

Poirion, Fabrice . . . . . . . . . . . . . . . . . . . 55

Polat, Leyla Ozgur . . . . . . . . . . . . . . . . . 84

Polat, Olcay . . . . . . . . . . . . . . . . . . . . . . 84

Porcelli, Margherita . . . . . . . . . . . . . . . . 98

Prieur, Clémentine . . . . . . . . . . . . . . . . 66

Prendergast, Kevin . . . . . . . . . . . . . . . . . 122

Poss, Michael . . . . . . . . . . . . . . . . . . . . 118

Puerto, Justo . . . . . . . . . . . . . . . . . . 93, 99

Pugliese, Luigi Di Puglia . . . . . . . . . . . . . 57

Pustelnik, Nelly . . . . . . . . . . . . . . . . . . . 116

R Raghavan, S. . . . . . . . . . . . . . . . . . . . . . 52

Rama, Paula . . . . . . . . . . . . . . . . . . . . . . 95

Ramos, Nestor . . . . . . . . . . . . . . . . . . . . 58

Ras, Charl . . . . . . . . . . . . . . . . . . . . . . . . 122

Ratli, Mustapha . . . . . . . . . . . . . . . . . . . 91

Rebelo, Rui Diogo . . . . . . . . . . . . . . . . .

Requejo, Cristina . . . . . . . . . . . . . . . 51, 119

Respício, Ana . . . . . . . . . . . . . . . . . . . . . 110

Richtarik, Peter . . . . . . . . . . . . . . . . . . . 104

Rinaldi, Giovanni . . . . . . . . . . . . . . . . . . 42

Rodrigues, Filipe . . . . . . . . . . . . . . . 51, 119

Rodrigues, Ana Maria . . . . . . . . . . . . . . 90

Rodrigues, Helena Sofia . . . . . . . . . . . . 125

Rodríguez-Chía, Antonio Manuel . . . . . 99

Rosasco, Lorenzo . . . . . . . . . . . . . . . . . . 115

Royer, Clément . . . . . . . . . . . . . . . . . . . 105

Ruiz-Hernández, Diego . . . . . . . . . . . . 79

Ruthmair, Mario . . . . . . . . . . . . . . . . 61, 62

I Indices


S Saldanha, Ricardo L. . . . . . . . . . . . . . 75, 94

Saldanha-da-Gama, Francisco . . . . . . . 79

Salmerón, José M. G. . . . . . . . . . . . . . . 103

Sampaio, Phillipe . . . . . . . . . . . . . . . . . 54

Santos, Daniel . . . . . . . . . . . . . . . . . 62, 113

Santos, José Luís . . . . . . . . . . . . . . . . . . 57

Santos, Marcio . . . . . . . . . . . . . . . . . . . 118

Santos, Nicolau . . . . . . . . . . . . . . . . . . . 108

Schönefeld, Klaus . . . . . . . . . . . . . . . . . 69

Seifert, Sarah . . . . . . . . . . . . . . . . . . . . . 69

Sellami, Lynda . . . . . . . . . . . . . . . . . . . . 124

Sengul, Seray . . . . . . . . . . . . . . . . . . . . . 86

Silva, Pedro M. . . . . . . . . . . . . . . . . . . . 106

Silva, Pedro Cristiano . . . . . . . . . . . . . . 94

Simić, S.K. . . . . . . . . . . . . . . . . . . . . . . . 95

Simonetti, Luidi . . . . . . . . . . . . . . . . . . . 82

Singh, Pitam . . . . . . . . . . . . . . . . . . . . . 57

Sinnl, Markus . . . . . . . . . . . . . . . . . . . . 62

Sinoquet, Delphine . . . . . . . . . . . . . . . . 66

Sousa, Amaro de . . . . . . . . . . . . . . . . . . 70

Stefanova, Maria . . . . . . . . . . . . . . . . . . 60

Stanić, Zoran . . . . . . . . . . . . . . . . . . . . . 95

Susu, Livia . . . . . . . . . . . . . . . . . . . . . . . 73

Swevers, Jan . . . . . . . . . . . . . . . . . . . . 53, 87

T

Tan, Pauline . . . . . . . . . . . . . . . . . . . . . . 116

Tchemisova, Tatiana . . . . . . . . . . . . . . . 81

Thomas, Doreen . . . . . . . . . . . . . . . . . . 122

Toint, Philippe L. . . . . . . . . . . . . . . . . . . 98

Torriero, Anna . . . . . . . . . . . . . . . . . . . . 121

Trautmann, Philip . . . . . . . . . . . . . . 72, 97

Trombettoni, Gilles . . . . . . . . . . . . . . . . . 87

U

Uschmajew, Andre . . . . . . . . . . . . . . . . . . 73

W

Walter, Daniel . . . . . . . . . . . . . . . . . . . . . 97

Wang, Jianmin . . . . . . . . . . . . . . . . . . . .

Wang, Yi . . . . . . . . . . . . . . . . . . . . . . . . . 75

Wendt, Herwig . . . . . . . . . . . . . . . . . . . . 116

Wright, Stephen . . . . . . . . . . . . . . . . . . . 105

V Vaiter, Samuel . . . . . . . . . . . . . . . . . . . . 116

Van Vyve, Mathieu . . . . . . . . . . . . . . . . 83

Vaz, Ismael . . . . . . . . . . . . . . . . . . . . . . . 56

Vaze, Vikrant . . . . . . . . . . . . . . . . . . . . . 91

Viana, Ana . . . . . . . . . . . . . . . . . . . . . . . 108

Vieira, Manuel . . . . . . . . . . . . . . . . . . . 100

Villa, Silvia . . . . . . . . . . . . . . . . . . . . . . . 115

Vocaturo, Francesca . . . . . . . . . . . . . . . 63

Y Youness, Rtimi . . . . . . . . . . . . . . . . . . . 59

Yurek, Emine Es . . . . . . . . . . . . . . . . . . . 120

Z

Zhang, Rui . . . . . . . . . . . . . . . . . . . . . . . 52

Zhang, Zaikun . . . . . . . . . . . . . . . . . . . . 66

Zilinskas, Julius . . . . . . . . . . . . . . . 101, 103

Indices


Presenting Authors Index

A

Agra, Agostinho (FA2) . . . . . . . . . .

Alessandretti, Andrea (WA2) . . . . .

Almeida, Maria Teresa (TA3) . . . . .

Amaral, Paula (TC1) . . . . . . . . . . . .

Aneja, Yash (WB4) . . . . . . . . . . . . . .

Antunes, António (WC4) . . . . . . . . .

Arulselvan, Ashwin (FA2) . . . . . . . .

Arya, Rubi (WA4) . . . . . . . . . . . . . . .

Atamna, Asma (WA3) . . . . . . . . . . .

Attila, Öykü Naz (FA2) . . . . . . . . . . .

Auger, Anne (TB4) . . . . . . . . . . . . . .

118

53

83

103

67

76

117

57

54

118

97

B

Bandeira, Luís Miguel (TA6) . . . . . .

Barbarosie, Cristian (WA3) . . . . . . .

Barbato, Michele (TC6) . . . . . . . . . .

Barbosa, Vitor (TC5) . . . . . . . . . . . .

Basto, João (WB4) . . . . . . . . . . . . . .

Benati, Stefano (TB5) . . . . . . . . . . .

Bergou, El Houcine (WB3) . . . . . . .

Bernardino, Raquel (TC6) . . . . . . . .

Bernardo, Marcella (WB2) . . . . . . .

Bomze, Immanuel (Plenary IV) . . .

Bostanabad, Mina Saee (TA2) . . . .

90

54

113

110

68

99

65

112

63

44

80

C

Cabezas, Xavier (TA1) . . . . . . . . . . .

Campi, Marco (Plenary III) . . . . . . .

Captivo, Maria Eugénia (TC4) . . . . .

Cardoso, Domingos M. (TB2) . . . . .

Carvalho, Ana Sofia (TC4) . . . . . . . .

Carvalho, Filipa Duarte de (TA3) . . .

Casado, Leocadio G. (TC1) . . . . . . . .

Castro, Pedro (TA5) . . . . . . . . . . . . .

Cavadas, Joana (TA6) . . . . . . . . . . .

Cerdeira, Jorge Orestes (TB2) . . . . .

Chen, Dehan (TB3) . . . . . . . . . . . . .

Christof, Constantin (WC2) . . . . . . .

78

43

109

95

107

81

103

88

91

94

96

72

Clemente, Gian Paolo (FA5) . . . . . . . . .

Corberan, Àngel (WB1) . . . . . . . . . . . .

Cornaro, Alessandra (FA4) . . . . . . . . . .

Correia, Isabel (TB6) . . . . . . . . . . . . . . .

Custódio, Ana Luísa (TB4) . . . . . . . . . .

123

63

121

101

98

D

De Mauri, Massimo (TA5) . . . . . . . . . . .

Duijkeren, Niels van (WA2) . . . . . . . . .

87

53

E

El Amri, Mohamed Reda (WB3) . . . . . .

Engel, Sebastian (WC2) . . . . . . . . . . . .

66

72

F

Farid, Mahboubeh (WC3) . . . . . . . . . . .

Filipecki, Bartosz (TA3) . . . . . . . . . . . . .

Fischer, Andreas (WB5) . . . . . . . . . . . .

Flores, Luís (FA2) . . . . . . . . . . . . . . . . . .

Fonseca, Maria da Conceição (TA4) . . .

Fontes, Dalila B.M.M. (FA4) . . . . . . . . .

Fontes, Fernando (WA2) . . . . . . . . . . .

Fortz, Bernard (WC1) . . . . . . . . . . . . . .

74

83

69

117

85

123

54

71

G

Gabl, Markus (TB1) . . . . . . . . . . . . . . . .

Gamarra, Luis Francisco Castillo (WA5)

Garmanjani, Rohollah (WC3) . . . . . . . .

Garrigos, Guillaume (FA1) . . . . . . . . . .

Gdowska, Katarzyna (TC5, FA3) . . . . . .

Gendron, Bernard (WC1) . . . . . . . . . . .

Gentile, Claudio (TC3) . . . . . . . . . . . . . .

Goldfarb, Donald (Plenary VI) . . . . . . .

Gonçalves, Graça (TB5) . . . . . . . . . . . . .

Gondzio, Jacek (Plenary V) . . . . . . . . . .

Gonzalez, Juan José Salazar (WA1) . . .

Goucha, António (TC3) . . . . . . . . . . . . .

Gower, Robert M. (TC2) . . . . . . . . . . . .

Groetzner, Patrick (TC1) . . . . . . . . . . . .

Guo, Peng (WC4) . . . . . . . . . . . . . . . . . .

93

58

74

115

111, 120

71

107

47

100

46

52

106

104

102

75

Indices


H

Homayouni, Seyed Mahdi (WB4) . .

Hosseini, Seyedehsomayeh (WC3) .

67

73

I

Iglésias, Carlos (WC4) . . . . . . . . . . .

Ishida, Hideshi (WA5) . . . . . . . . . . .

75

60

J

Josz, Cédric (TA2) . . . . . . . . . . . . . . 81

K

Kahr, Michael (TB1) . . . . . . . . . . . .

Klimentova, Xenia (TC4) . . . . . . . . .

Knauer, Matthias (WB2) . . . . . . . . .

Kruse, Florian (TB3) . . . . . . . . . . . . .

92

108

64

96

L

Labbé, Martine (Plenary I) . . . . . . .

Lancinskas, Algirdas (TB6) . . . . . . . .

Leitner, Markus (WC1) . . . . . . . . . .

Liang, Jingwei (FA1) . . . . . . . . . . . . .

Ljubic, Ivana (WB1, TA1) . . . . . . . . .

Lucena, Abilio (FA5) . . . . . . . . . . . . .

Luz, Carlos J. (TB2) . . . . . . . . . . . . . .

41

101

70

115

62, 78

125

95

M

Madriz, Eleazar (TC3) . . . . . . . . . . .

Mahjoub, Ridha (WA1) . . . . . . . . . .

Martins, Pedro (WA1) . . . . . . . . . . .

Menezes, Mozart B.C. (TA1) . . . . . .

Mercier, Quentin (WA3) . . . . . . . . .

Mesquita, Marta (TC5) . . . . . . . . . .

Messine, Frederic (TA5) . . . . . . . . .

Monteiro, M. Teresa T. (FA5) . . . . .

Mourão, Maria Cândida (TA4) . . . .

Mumcuoglu, Melis (TA5) . . . . . . . . .

105

52

51

79

55

111

88

125

86

86

P

Paiva, Luís Tiago (WB2) . . . . . . . . . .

Paredes-Belmar, Germán (FA3) . . .

Pascoal, Marta (WA4) . . . . . . . . . . .

Pawuels, Benoît (WB3) . . . . . . . . . .

Pehlivan, Nimet Yapici (WA4) . . . . .

Perchet, Vianney (TC2) . . . . . . . . . .

Pesneau, Pierre (WB1) . . . . . . . . . .

64

121

58

66

56

104

62

Phusingha, Saranthorn (TA6) . . . . . . . .

Polat, Leyla Ozgur (TA4) . . . . . . . . . . . .

Polat, Olcay (TA4) . . . . . . . . . . . . . . . . . .

Porcelli, Margherita (TB4) . . . . . . . . . . .

Prendergast, Kevin (FA4) . . . . . . . . . . . .

Puerto, Justo (TB1) . . . . . . . . . . . . . . . .

Pustelnik, Nelly (FA1) . . . . . . . . . . . . . .

89

84

84

98

122

93

116

R

Raghavan, S. (WA1) . . . . . . . . . . . . . . . .

Ratli, Mustapha (TA6) . . . . . . . . . . . . . .

Requejo, Cristina (WA1) . . . . . . . . . . . .

Rinaldi, Giovanni (Plenary II) . . . . . . . .

Rodrigues, Filipe (FA3) . . . . . . . . . . . . .

Rodríguez-Chía, Antonio Manuel (TB5)

Royer, Clément (TC2) . . . . . . . . . . . . . . .

Ruthmair, Mario (WB1) . . . . . . . . . . . . .

52

91

51

42

119

99

105

61

S

Saldanha-da-Gama, Francisco (TA1) . . .

Santos, Daniel (TC6) . . . . . . . . . . . . . . .

Santos, José Luís (WA4) . . . . . . . . . . . .

Schönefeld, Klaus (WB5) . . . . . . . . . . . .

Sellami, Lynda (FA5) . . . . . . . . . . . . . . .

Simonetti, Luidi (TA3) . . . . . . . . . . . . . .

Sousa, Amaro de (WC1) . . . . . . . . . . . .

Stefanova, Maria (WA5) . . . . . . . . . . . .

Susu, Livia (WC2) . . . . . . . . . . . . . . . . . .

79

113

57

69

124

82

70

60

73

T

Tchemisova, Tatiana (TA2) . . . . . . . . . .

Trautmann, Philip (TB3) . . . . . . . . . . . . .

81

97

V

Vaiter, Samuel (FA1) . . . . . . . . . . . . . . .

Vaz, Ismael (WA3) . . . . . . . . . . . . . . . . .

Vieira, Manuel (TB6) . . . . . . . . . . . . . . .

116

56

100

Y

Youness, Rtimi (WA5) . . . . . . . . . . . . . .

Yurek, Emine Es (FA3) . . . . . . . . . . . . . .

59

120

Indices


Session Chairs Index

Agra, Agostinho (FA2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Almeida, Teresa (TA3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Amaral, Paula (Plenary IV, TB1, TC1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44, 92, 102

Antunes, António (WC4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Basto, João (WB4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Captivo, Maria Eugénia (TC4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Cardoso, Domingos M. (Plenary V, TB2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46, 94

Castro, Pedro (TA5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Cavadas, Joana (TA6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

Corberan, Àngel (WB1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

Correia, Isabel (TB6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Fischer, Andreas (WB5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Fontes, Dalila B.M.M. (FA4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Fontes, Fernando (Plenary III, WA2, WB2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43, 53, 63

Fortz, Bernard (WC1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Garmanjani, Rohollah (WC3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Gentile, Claudio (TC3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Gonçalves, Graça (TB5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Gonzalez, Juan José Salazar (WA1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Gouveia, Luís (Plenary II) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Ishida, Hideshi (WA5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Lucena, Abílio (FA5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

Mesquita, Marta (TC5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

Mourão, Maria Cândida (TA4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Paredes-Belmar, Germán (FA3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

Pascoal, Marta (WA4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Pawuels, Benoît (WB3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Porcelli, Margherita (TB4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Royer, Clément (TC2, FA1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104, 115

Saldanha-da-Gama, Francisco (Plenary I, TA1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41, 78

Santos, Daniel (TC6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Susu, Livia (WC2, TB3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72, 96

Tchemisova, Tatiana (TA2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Vaz, Ismael (WA3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Vicente, Luís Nunes (Plenary VI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Indices


Sessions Index

Plenary I Plenary II Plenary III Plenary IV Plenary V Plenary VI

Stackelberg games and bilevel bilinear optimization problem . . . . . . . . . . . . . . . . . . Quadratic unconstrained binary optimization: some exact and heuristic approaches Scenario optimization: how far can we trust data-based decisions? . . . . . . . . . . . . . On gaps and dots - duality and attainability in conic optimization . . . . . . . . . . . . . . Continuation in optimization: From interior point methods to big data . . . . . . . . . . Quasi-Newton methods: block updates, adaptive step sizes, and stochastic variants

41 42 43 44 46 47

WA1 WA2 WA3 WA4 WA5

Workshop Luís Gouveia Session I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization-Based Control I: Fundamentals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Continuous Constrained Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multiobjective Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization in Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 53 54 56 58

WB1 WB2 WB3 WB4 WB5

Workshop Luís Gouveia Session II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization-Based Control II: Algorithms and Applications . . . . . . . . . . . . . . . . . . . . . . Nonlinear Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Production Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equilibrium and Complementarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

61 63 65 67 69

WC1 WC2 WC3 WC4

Workshop Luís Gouveia Session III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variational Inequalities and PDE-Constrained Optimization I . . . . . . . . . . . . . . . . . . . . . Continuous Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Railway Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

70 72 73 75

TA1 TA2 TA3 TA4 TA5 TA6

Facility Location with Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Semidefinite and Semi-infinite Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Networks I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Routing I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Non-Linear MIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sectorization and Parking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

78 80 81 84 86 89

TB1 TB2 TB3 TB4 TB5 TB6

Copositive Optimization I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graphs and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variational Inequalities and PDE-Constrained Optimization II . . . . . . . . . . . . . . . . . . . . . Derivative Free Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Facility Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

92 94 96 97 99

100

TC1 TC2 TC3 TC4 TC5 TC6

Copositive Optimization II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stochastic and Randomized Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Health Care Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Urban Transportation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Travelling Salesman Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

102 104 105 107 110 112

FA1 FA2 FA3 FA4 FA5

Recent Advances in First-Order Methods and Applications . . . . . . . . . . . . . . . . . . . . . . . Mixed Integer Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Routing II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Networks II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimization Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

115 117 119 121 123

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